Forem: Vivek0712

Introducing AgentRegistry: Centralized, Flexible Agent Management for Strands Agents SDK

Vivek0712 — Tue, 08 Jul 2025 16:44:58 +0000

Introduction

As AI agent systems evolve, the need for modular, scalable, and manageable agent orchestration becomes paramount. In my latest contribution to the Strands Agents SDK (currently under review), I introduce the AgentRegistry—a feature designed to bring order, flexibility, and power to agent management, mirroring the successful ToolRegistry pattern.

This blog post is a comprehensive, developer-focused deep dive into the what, why, how, and when of AgentRegistry. We’ll cover its motivation, design, features, comparisons, and practical usage, with full code examples and implementation details.

PR is currently under review. Stay tuned for updates and documentation examples!
⭐️ My implementation here
Feature Request Issue Created in Official repo
PR Status

Why Do We Need an Agent Registry?

The Problem

Single-agent workflows are simple—but as soon as you want multiple agents (collaborative, specialized, or per-session), things get messy.
Manual agent management (lists, dicts, passing references) is error-prone and hard to scale.
Session-based or multi-user applications (e.g., chatbots, workflow engines) need to keep track of which agents are active, and where.
Tool specialization and security: Sometimes, you want certain agents to have access to only a subset of tools.

The Solution

A central registry for agents, just like for tools, solves these problems by providing:

Centralized lookup and management of all agents in your application.
Session or event loop scoping, so you can group agents per user, conversation, or workflow.
Specialization and isolation by mapping subsets of tools to specific agents.
Clean, optional integration—no breaking changes for simple, single-agent use cases.

What is AgentRegistry?

AgentRegistry is a lightweight, optional utility class that:

Registers and discovers agents by name (or auto-generated name).
Optionally groups agents by session or event loop.
Allows mapping a subset of tools to each agent.
Integrates cleanly with agent state and event loop patterns.
Is fully backward compatible and opt-in.

It is inspired by the ToolRegistry pattern, but for agents.

Feature-by-Feature Breakdown

1. Centralized Registration and Discovery

What:

All agents are registered in a single place. You can easily add, find, or remove agents by name.

Why:

Simplifies orchestration in multi-agent systems.
Avoids passing agent references everywhere.
Makes debugging and monitoring easier.

How:

from strands.agent.agent_registry import AgentRegistry

agent_registry = AgentRegistry()
agent_registry.register_agent(my_agent, name="assistant")
agent = agent_registry.get_agent("assistant")

Comparison:

Without registry: You manage agents in your own dict/list.
With registry: The SDK manages it for you, with a clean API.

2. Optional Session-Scoped Management

What:

Group agents by session or event loop. This is perfect for multi-user or multi-conversation systems.

Why:

Supports scenarios where you have multiple users or conversations, each with their own set of agents.
Makes it easy to clean up agents when a session ends.
Prevents cross-session data leakage or confusion.

How:

agent_registry.register_agent(my_agent, name="assistant", session_id="session123")
session_agents = agent_registry.list_agents(session_id="session123")

Comparison:

Without registry: You must manually track which agents belong to which session.
With registry: The registry handles session grouping and lookup.

3. Tool Subset Mapping for Specialization

What:

Assign a unique set of tools to each agent, supporting specialization and security.

Why:

Enables agent specialization (e.g., one agent can only use math tools, another can only use web search).
Improves security and isolation (agents can’t access tools they shouldn’t).
Makes it easy to reason about and test agent capabilities.

How:

from strands_tools import calculator, web_search

agent_registry.register_agent(
    my_agent,
    name="math_agent",
    tool_subset=[calculator]
)
agent_registry.register_agent(
    another_agent,
    name="search_agent",
    tool_subset=[web_search]
)

Comparison:

Without registry: You must ensure each agent is constructed with the right tools and keep track yourself.
With registry: The registry can enforce and manage tool subsets per agent.

4. Clean, Lightweight, and Optional Integration

What:

No forced changes to the default single-agent workflow.
No extra overhead if you don’t need advanced features.
Separation of concerns: agent state and event loop logic remain untouched.

Why:

Maintains the separation of concerns: agent logic, state, and event loop are not coupled to the registry.
Keeps the codebase clean and maintainable.
Advanced features are available, but only if you opt in.

How:

If you use a single agent:

agent = Agent(...)
# No registry needed!

If you want advanced management:

agent_registry = AgentRegistry()
agent_registry.register_agent(agent, ...)

Comparison:

Without registry: No change to your workflow.
With registry: You get advanced management when you need it.

5. Backward Compatible and Developer Friendly

What:

If you don’t use the registry, everything works as before.
Advanced users can opt-in for powerful orchestration and management.

Why:

Ensures backward compatibility.
Keeps the learning curve low for new users.
Power users can leverage the registry for complex workflows without affecting others.

How:

Default usage (no registry):

agent = Agent(tools=[...])
result = agent("Hello!")

Advanced usage (with registry):

agent_registry = AgentRegistry()
agent_registry.register_agent(Agent(tools=[...]), name="assistant", session_id="user1")

Intrinsic Details: Naming, Auto-Generation, and Retrieval

How Are Agent Names Handled?

By default, agents in Strands Agents SDK do not have a name.
When registering with the registry, you can provide a name, or let the registry auto-generate one.
The registry uses:

  agent_name = name or getattr(agent, 'name', None) or f"agent_{uuid4().hex[:8]}"

If you provide a name, it uses that.
If the agent has a name attribute, it uses that.
Otherwise, it generates a unique name like "agent_1a2b3c4d".

Best Practice:

Provide a name when registering for clarity.
If you don’t, capture the return value of register_agent() to refer to the agent later.

Example:

auto_name = agent_registry.register_agent(agent)
retrieved_agent = agent_registry.get_agent(auto_name)

Full Example: Multi-Agent, Session-Scoped Workflow

Here’s a full-featured example that demonstrates all the core functionalities of AgentRegistry in a multi-agent orchestration scenario:

This example demonstrates:

Registering agents with and without tool subsets (showing both patterns).
Listing all registered agents at any time.
Accessing the tool registry for each agent, showing which tools are available (either via subset or the agent’s own list).
Wrapping agents as tools for orchestration, so the orchestrator can delegate tasks.
Unregistering a specific agent and verifying the change.
Clearing the registry and confirming all agents are removed.

from strands import Agent, tool
from strands_tools import calculator, web_search
from strands.agent.agent_registry import AgentRegistry

# 1. Define system prompts for each specialized agent
RESEARCH_PROMPT = "You are a research assistant. Provide factual, well-sourced answers."
MATH_PROMPT = "You are a math expert. Solve math problems accurately."
SEARCH_PROMPT = "You are a web search specialist. Find up-to-date information online."

# 2. Create specialized agents
research_agent = Agent(system_prompt=RESEARCH_PROMPT, tools=[web_search])
math_agent = Agent(system_prompt=MATH_PROMPT, tools=[calculator])
search_agent = Agent(system_prompt=SEARCH_PROMPT, tools=[web_search, calculator])  # Multi-tool agent

# 3. Register agents in the AgentRegistry, with and without tool subsets
agent_registry = AgentRegistry()
agent_registry.register_agent(research_agent, name="research_agent", tool_subset=[web_search])
agent_registry.register_agent(math_agent, name="math_agent", tool_subset=[calculator])
agent_registry.register_agent(search_agent, name="search_agent")  # No subset, uses all provided tools

# 4. List all registered agents
print("Registered agents:", agent_registry.list_agents())

# 5. Access the tool registry for a specific agent
for agent_name in agent_registry.list_agents():
    tool_registry = agent_registry.get_tool_registry_for_agent(agent_name)
    if tool_registry:
        print(f"Tools for {agent_name}: {tool_registry.tool_names}")
    else:
        print(f"{agent_name} uses its own tool list (no registry subset)")

# 6. Wrap agents as tools for orchestration
@tool
def research_tool(query: str) -> str:
    """Delegate research queries to the research agent."""
    agent = agent_registry.get_agent("research_agent")
    return str(agent(query))

@tool
def math_tool(query: str) -> str:
    """Delegate math queries to the math agent."""
    agent = agent_registry.get_agent("math_agent")
    return str(agent(query))

@tool
def search_tool(query: str) -> str:
    """Delegate search queries to the search agent."""
    agent = agent_registry.get_agent("search_agent")
    return str(agent(query))

# 7. Orchestrator agent with access to agent-tools
ORCHESTRATOR_PROMPT = """
You are an orchestrator. Route queries to the correct specialized agent:
- Use research_tool for research questions
- Use math_tool for math problems
- Use search_tool for web searches
- Answer directly if no tool is needed
"""

orchestrator = Agent(
    system_prompt=ORCHESTRATOR_PROMPT,
    tools=[research_tool, math_tool, search_tool]
)

# 8. Example usage
print("\n=== Orchestrator handles a research query ===")
print(orchestrator("What are the latest advancements in quantum computing?"))

print("\n=== Orchestrator handles a math query ===")
print(orchestrator("What is the square root of 2025?"))

print("\n=== Orchestrator handles a web search query ===")
print(orchestrator("Find the current weather in Tokyo."))

# 9. Unregister a particular agent
agent_registry.unregister_agent("math_agent")
print("\nAfter unregistering math_agent, registered agents:", agent_registry.list_agents())

# 10. Clear the registry
agent_registry.clear()
print("After clearing, registered agents:", agent_registry.list_agents())

When Should You Use AgentRegistry?

Single-agent, simple scripts:

You don’t need it! Just use Agent as before.
Multi-agent, multi-session, or advanced orchestration:

AgentRegistry is your friend. It keeps your code clean, modular, and scalable.

Developer Insights

Extensible: You can add metadata, hooks, or analytics to the registry in the future.
Safe: No agent state is stored in the registry—each agent manages its own state.
Flexible: You can use the registry for global, per-session, or even per-task agent management.
Auto-naming: If you don’t provide a name, the registry generates a unique one and returns it, so you can always refer to your agent.

Comparison Table

Feature	Without AgentRegistry	With AgentRegistry
Agent lookup	Manual dict/list	Centralized, by name/session
Session grouping	Manual, error-prone	Built-in, easy
Tool specialization	Manual, per-agent	Enforced, per-agent via registry
Naming/uniqueness	Up to you	Auto-generated or user-provided
Backward compatibility	N/A	100% preserved
Overhead for simple use	None	None (if not used)
Extensibility	Manual	Registry pattern, easy to extend

Conclusion

The new AgentRegistry brings the Strands Agents SDK one step closer to being a truly modular, production-ready platform for building sophisticated AI agent systems. It’s developer-friendly, backward compatible, and powerful—and it’s available as an opt-in utility for those who need it.

Try it out, give feedback, and help shape the future of agent orchestration in Strands-agents!

🕵️‍♂️ Building a Cybersecurity CTF Game with Amazon Q Developer CLI

Vivek0712 — Sun, 29 Jun 2025 19:59:14 +0000

🕵️‍♂️ Building a Cybersecurity CTF Game with Amazon Q Developer CLI

🌟 Why I Built a Terminal-Based Digital Forensics Game

I wanted to build more than just a puzzle. My goal was to create an immersive simulation that helps players develop real cybersecurity skills through hands-on interaction — under pressure.

The mission:

Simulate a real-world incident response scenario
Create a UNIX-style terminal interface
Teach players digital forensics and debugging
Increase challenge over time with progressive corruption

The result? BlackDOS Terminal — a Pygame-powered game that drops players into a retro terminal interface, giving them 15 minutes to extract evidence before the system collapses.

View the Full Walkthrough Video here: https://www.youtube.com/watch?v=8VvDf0qFb7k
View Github Repo here: https://github.com/Vivek0712/ctfgame-amazonq

🤖 How Amazon Q Developer CLI Helped Me Build This Game

✅ My Prompting Strategy

Rather than asking for everything upfront, I used a layered prompting approach:

First prompt:

You have received a suspicious file named `shadow_terminal.py`, and your job is to investigate it. When executed, it opens a Pygame-rendered fake terminal environment simulating an old-school UNIX-like system called BlackDOS. The interface mimics a real terminal with a blinking cursor, command input, and text-based output, but something feels off. You are tasked with uncovering the backdoor left by an insider and capturing the hidden flag. The fake terminal accepts commands like `ls`, `cat`, `ps`, `whoami`, `history`, `strings`, `ssh`, `debug`, and `dump`, some of which behave normally while others are glitched, non-functional, or reveal misleading information. The filesystem lists files such as `note.txt`, `.hidden_log`, `blacknet_config.sys`, and `shadow_engine.bin`. Hidden files are not visible unless the player uses `ls -a`. Viewing `.hidden_log` reveals a cryptic hint about corrupted memory regions. Using `history` shows previously typed commands, but one line appears obfuscated using XOR, which must be decoded with a custom command `decode -x`. Running `debug shadow_engine.bin` opens a fake debugger UI inside the same Pygame interface with commands like `break 0x0040`, `dump mem 0x0030:0x0050`, and `strings shadow_engine.bin`. These expose low-level clues including a base64-encoded message `U0hBRE9Xf0JBU0VMTElORV9FTUlMRU9SRV9URU1Q`. The player must decode this to continue. Additionally, reading `blacknet_config.sys` reveals image-like ASCII content mimicking PNG headers, indicating hidden steganographic data. A fake `decode_image` command reveals characters embedded in a pixel-art grid which, when combined with memory dumps and debugger output, reveal the full flag: SHADOW{BASELLINE\_EMILIORE\_TEMP}. Throughout the game, the terminal occasionally flickers or simulates a ghost user typing commands, adding to the immersion. The entire game is keyboard-only with mouse disabled, and the screen includes simulated glitches, corrupted text, and a fake RAM dump view. The challenge tests the player's knowledge of file hiding, XOR decoding, base64, fake debugger interaction, command-line forensics, and basic steganography, all within a convincingly disguised Pygame terminal.

Then:

Add system corruption that makes commands glitch as time progresses. Add visual effects and fake system crashes.

Finally:

Add tab completion, command history, contextual hints, and better error messages.

💡 Prompting Techniques That Worked

Give context and goals, not just features
Explain why the feature matters (e.g., urgency, realism, learning value)
Ask for implementation options and rationale
Build iteratively: core → corruption → polish

🧠 How Amazon Q Developer CLI Solved Key Challenges

🧵 1. Managing Game State

Amazon Q CLI generated a TerminalState class that tracked progress, system health, and file visibility:

class TerminalState:
    def __init__(self):
        self.investigation_progress = {
            'read_note': False,
            'found_hidden_files': False,
            'decoded_xor': False,
            'used_debugger': False,
            'extracted_memory': False,
            'decoded_image': False
        }
        self.corruption_level = 0
        self.hints_given = 0
        self.files = {...}

🎨 2. Real-Time Visual Corruption

It applied glitch effects probabilistically so gameplay remained smooth:

def apply_corruption(self, text):
    if not text or random.random() < 0.3:
        return text
    effects = [...]
    return random.choice(effects)(text)

🧰 3. Realistic Command Simulation

It created GDB-style debugger output to teach real forensics flow:

def cmd_debug(self, args):
    self.add_line("Loading GNU gdb (BlackDOS) 8.1...")
    self.add_line("Reading symbols from shadow_engine.bin...")
    self.add_line("Available commands: dump mem, break, run, read")

⏰ Game Systems & Mechanics

🗂️ Filesystem & Terminal UI

Players interact using ls, cat, debug, strings, history, decode, etc.

Files include:

note.txt
.hidden_log
blacknet_config.sys
shadow_engine.bin

Using ls -a reveals hidden files, and viewing history uncovers a corrupted command line that must be XOR-decoded.

🔍 Memory Dump Example

Inside debug mode:

dump mem 0x0030:0x0048

Sample output:

0x0030: 53 48 41 44 4f 57 7b 42   |SHADOW{B|
0x0038: 41 53 45 4c 4c 49 4e 45   |ASELINE|
0x0040: 5f 45 4d 49 4c 49 4f 52   |_EMILIOR|
0x0048: 45 5f 54 45 4d 50 7d 00   |E_TEMP}|

🧠 Adaptive Hint System

Hints are based on your investigation progress:

def cmd_hint(self):
    if not progress['read_note']:
        hint = "Start with: cat note.txt"
    elif not progress['found_hidden_files']:
        hint = "Try: ls -a"

🛠️ System Corruption

The game gets harder as time runs out:

def update_corruption_level(self):
    elapsed = time.time() - self.start_time
    level = min(5, int((elapsed - 600) / 120))
    self.corruption_level = level

Stages:

Normal
Minor glitches
Terminal flickers and delays
Command failures
System crashes

🧭 Tab Completion & Command History

You can press Tab to autocomplete commands and filenames, just like a real terminal:

def get_completions(self, partial):
    # Suggest commands or file names

⚙️ Time-Saving Features Built by Amazon Q Developer CLI

✅ Testing scripts to validate syntax and dependencies
🔧 Helpful error handling with try-except blocks
📄 Auto-generated README and in-game command help
🧠 Smart command dispatcher using getattr()
📁 Fake forensic files built into the narrative

🎯 What Players Learn

Technical Skills

Linux terminal navigation
XOR and Base64 decoding
Binary memory analysis
GDB-style debugging
File system investigation

Soft Skills

Working under pressure
Pattern recognition
Logical investigation
Digital patience

📊 Dev Metrics

Total time: 8 hours
Lines of code: ~1,200
Amazon Q Developer CLI generated ~70%
Manual polish: ~30%
Time saved: 15–20 hours

🧪 Future Features

Multiplayer cooperative mode
Procedurally generated evidence
Network and registry forensics
Scoring system for competitions
In-game certifications and achievements

🎉 Final Thoughts

BlackDOS Terminal is more than a game — it's an immersive learning experience powered by Amazon Q Developer CLI.

With the right prompts and a clear educational goal, I was able to:

Build realistic systems quickly
Automate boilerplate tasks
Keep the game engaging and informative

Want to try it?

Run python shadow_terminal.py and see if you can extract the flag before the system collapses.

If you're building developer tools, simulations, or training platforms, consider using Amazon Q Developer CLI as your secret weapon.

Happy hacking! 🔍💻🧠

From a Curious Student to Meeting Satya Nadella: My Journey with the Azure Developer Community

Vivek0712 — Sun, 16 Feb 2025 12:42:23 +0000

In late 2020, I was just a curious college student with a thirst for knowledge about cloud technologies and artificial intelligence. My journey began with a decision that many would call impulsive but turned out to be life-changing. I traveled over 500 kilometers just to attend a Microsoft Azure event. That one step of courage and curiosity marked the beginning of an extraordinary adventure.

🚀 The First Leap of Faith

I still remember the excitement mixed with nervousness as I entered the event venue. I knew no one there, but the warmth of the Azure Developer Community made me feel at home. I met developers, architects, and enthusiasts who were more than happy to share their knowledge. It was this openness that fueled my passion for cloud technologies.

🌱 Community: The Catalyst for Growth

The Azure Developer Community officially began in late 2020 and quickly became my launchpad for growth. It didn't just teach me technical skills; it taught me the essence of collaboration and continuous learning. I actively sought mentorship, participated in discussions, and never hesitated to ask questions. Over time, my efforts bore fruit. I was invited to speak at small meetups, and soon, these meetups turned into large-scale events and conferences.

Fast forward to today: I've spoken, hosted, or organized more than 300 events across the globe. These events range from intimate bootcamps to large-scale conferences with thousands of attendees. I even had the privilege of winning the AI ODC Challenge in both 2024 and 2025, which opened doors to new opportunities and collaborations.

🤝 Meeting the Visionary: Satya Nadella

One of the most surreal moments of my journey was meeting Satya Nadella, the CEO of Microsoft, not once but twice. The moment I shook hands with him, I felt the weight of years of learning and hard work crystallizing into a single, unforgettable experience. Getting photographed with him was a dream come true, and the picture was later featured in Pune Chaak's keynote session during the Microsoft AI Tour 2024 — a moment of recognition that made me realize the impact of my work and the power of community-driven growth.

❤️ Giving Back to the Community

The community that once nurtured me is now the one I contribute to passionately. I’ve mentored aspiring developers, organized countless bootcamps, and supported the growth of the Azure Developer Community in Tamil Nadu, which has now grown to over 5,000 members in record time.

🗣️ Talks and Sessions: A Chronicle of Knowledge Sharing

Throughout my journey, I've delivered numerous talks and conducted hands-on sessions. Here are some notable ones related to Azure and the Azure Developer Community:

Image Data Analytics using Python – Mepco Schlenk Engineering College
- GitHub Repo
Deep Dive into Microsoft Azure Automated Machine Learning – Global Azure Datafest 2020
- Event Link
Machine Learning Pipelines in AzureML – Azure Developer Community Tamil Nadu
- RSVP
Demo on Azure Cognitive Services – Azure ML Series
- GitHub Repo
Training & Deploying ML Models in Azure – Azure Developer Community Mumbai
- YouTube
Exploring Azure Cognitive Services – Azure Developer Community Raipur
- YouTube
Getting started with Azure AutoML – Azure Developer Community Chandigarh
- YouTube
Deploying Python Apps to AKS – Azure Developer Community Tamil Nadu
- YouTube

🎥 Video Sessions:

📝 Blog Posts on Dev.to

Attendance System with Face Mask Detection using Azure Face API (Published: Feb 15, 2024)
Predict NYC Taxi Fares | End-to-end Azure MLOps with GitHub Actions (Published: Feb 14, 2024)
Build a Production LLM-Powered QC Visual Inspection System Using Azure AI Foundry (Published: Feb 13, 2024)
Analyse Customer Sentiment using Azure Data Factory pipeline (Published: Feb 12, 2024)
Electronics-Datasheet-GPT: A Step-by-Step Guide to Building Your Own Datasheet Q&A AI Agent (Published: Feb 11, 2024)
Experimenting with DeepSeek-R1 Model in VS Code Hosted via GitHub Models (Published: Feb 10, 2024)
How AWS GenAI boosted my day-to-day Productivity (Published: May 31, 2024) (AWS Heroes)
What If Amazon Prime A/B Tests IPL Broadcasting? (Published: Feb 21, 2024)
Speed-Dataing for hackers! (Published: Jan 6, 2024)
Transforming Madurai | Tech, Community and more... (Published: Apr 17, 2023)
AWS Community Day Hyderabad 2023 | Experience with Vivek (Published: Mar 25, 2023) (AWS Heroes)
Blur Personal Identifiable Information (PII) in Text, Images and Videos (Published: Jun 10, 2022) (AWS Heroes)
Adding Chaos to ML Compute Targets (Published: Feb 15, 2022) (Azure Architecture Series)
Introducing Chaos Engineering to Machine Learning deployments (Published: Feb 12, 2022) (Azure Architecture Series)
How FORMULA 1 insights are powered by AWS (Published: Oct 20, 2021) (AWS Community Builders)
Hosting Python Packages in Azure DevOps (Published: Aug 31, 2021)
Training Machine Learning models in AzureML (Published: Aug 22, 2021) (Azure Machine Learning Series)
Getting started with Azure Machine Learning (Published: Aug 20, 2021) (Azure Machine Learning Series)
Introduction to Machine Learning (Published: Aug 19, 2021) (Azure Machine Learning Series)
Fine-tuning the performance of the DeepRacer model (Published: Jul 31, 2021) (AWS Community Builders - DeepRacer)
Create, Train, Race your first AWS DeepRacer Model (Published: Jul 24, 2021) (AWS Community Builders - DeepRacer)
Detecting and Visualizing Twitter Sentiment during COVID-19 Pandemic using AWS Comprehend and Quicksight (Published: Jul 19, 2021) (AWS Community Builders)
Creating COVID19 stats newsletter using Azure Function Apps (Published: Apr 14, 2021)
Literature Text Translation & Audio Synthesis using Microsoft Azure Cognitive Services

Platform: All posts are published on dev.to.

🎓 Certifications: The Foundation of My Technical Knowledge

My passion for continuous learning is reflected in the certifications I've earned:

Microsoft Azure:

Microsoft Certified Azure Developer Associate (AZ-204)
Microsoft Certified Azure Data Scientist Associate (DP-100)
Microsoft Certified Azure AI Engineer Associate (AI-100)
Microsoft Certified Azure Data Engineer Associate (DP-200 & DP-201)
Microsoft Certified Azure Fundamentals (AZ-900)

🌐 VickyBytes: A New Chapter

As I reflect on this journey, I’m thrilled to share that I'm working closely with Microsoft's leadership to build a new platform called VickyBytes. This initiative aims to provide developers with curated, high-quality content to stay ahead in the ever-evolving tech landscape. The idea is simple yet powerful: leverage the strength of community to empower developers worldwide.

👨‍💼 My Professional Journey: A Snapshot

VP of Product at Nexstem (YourStory Tech30 startup winner; Raised $3.5M!)
AWS ML Hero (Youngest ever!), AWS Hero of the Year (APJ 2024)
Invent & Simplify Award recipient at AWS Community Leaders Summit 2022
Founder & Lead: Azure Developer Community TN (Since 2021!)
Founder & Lead: AWS User Group Madurai (Nominated as Best UG 2022, 2023 & 2024)
Guinness World Record Holder (Thanks to AWS!)
Published Researcher: Recent paper at IIM-B & holder of 2 AI patents
80+ Hackathon Wins; Participant & Jury
Mentor | Blogger | Content Creator
Movie Buff | Go-Kart Racer | Guitar Enthusiast | Professional Reels Sender

🌟 Lessons from the Journey

Community is Everything: Growth accelerates when like-minded people collaborate.
Consistency Pays Off: Showing up consistently to learn, share, and contribute can open unimaginable doors.
Give More Than You Take: The more you give to the community, the more you grow.

🙏 A Heartfelt Thank You

I owe my success to the Azure Developer Community and the incredible people who believed in me when I was just a student with dreams. To every mentor, attendee, and fellow enthusiast: thank you for being part of my story.

Photo Gallery (Random moments clicked!)

This journey from being a curious student to becoming one of India's recognized AI figures has been nothing short of magical. And if there's one message I'd like to share, it’s this: take that first step, no matter how uncertain it seems. The community will catch you, guide you, and help you soar.

#AzureCommunity #AIInnovation #CommunityFirst #VickyBytes

Attendance System with Face Mask Detection using Azure Face API

Vivek0712 — Sat, 15 Feb 2025 08:55:43 +0000

In the post-pandemic world, many organizations require their staff or visitors to wear masks in addition to verifying identity for attendance. This blog post explains how to build a simple Face Recognition Attendance System with Face Mask Detection using Azure AI Face API.

With Azure AI Face, we can:

Detect faces in an image.
Check whether a person is wearing a mask (and if nose and mouth are covered).
Identify if a detected face is in our known list of authorized persons.
Mark attendance only if the person is recognized and wearing a mask.

This blog includes two main code snippets:

Recognizing a face and checking if it belongs to a trained group of authorized persons.
Detecting whether a person is wearing a mask and marking attendance.

Prerequisites

Azure Subscription
- You need an active Azure subscription to create an Azure Cognitive Services resource with the Face API.

Python 3.7+
- Ensure Python 3.7 (or higher) is installed on your system.
Azure AI Vision (Face) SDK
- Install via:
```
 pip install azure-ai-vision
```
Face API Key & Endpoint
- After creating a Face resource in Azure Portal, retrieve:
  - Endpoint (e.g., https://<resource-name>.cognitiveservices.azure.com/)
  - API Key (a 32-character key)

Architecture at a Glance

Large Person Group (LPG):
- You create a Large Person Group to store known persons (e.g., employees, students).
- Train the LPG so it can recognize these faces later.
Detect & Identify:
- When someone attempts to mark attendance, the system uploads a photo.
- Azure Face detects faces, returns attributes including Face ID, Mask properties, and (optionally) the quality for recognition.
- If recognized above a certain confidence threshold and wearing a mask, the system marks attendance.
Real-world Enhancements:
- Use a camera feed to capture images in real time.
- Integrate with your existing attendance software or a database.

Code Snippet #1: Door Access Control With Face Recognition

This snippet shows how to detect a face and check if it’s recognized in a previously trained Large Person Group. If recognized, we simulate opening a door by printing a welcome message. Otherwise, we deny access.

File Name: door_access_control_local_upload.py

import os
from azure.core.credentials import AzureKeyCredential
from azure.ai.vision.face import FaceAdministrationClient, FaceClient
from azure.ai.vision.face.models import (
    FaceAttributeTypeRecognition04,
    FaceDetectionModel,
    FaceRecognitionModel,
    QualityForRecognition
)

FACE_API_KEY = os.environ.get("FACE_APIKEY")  # Or replace with your API key as a string
FACE_ENDPOINT = os.environ.get("FACE_ENDPOINT")  # Or replace with your endpoint

# Must match the ID you used when creating/training the Large Person Group
LARGE_PERSON_GROUP_ID = "my-lpg-local-upload"

def check_access(face_client, face_id, confidence_threshold=0.7):
    """
    Identify the face_id against the Large Person Group.
    Return (True, person_id) if recognized >= threshold, else (False, None).
    """
    identify_results = face_client.identify_from_large_person_group(
        face_ids=[face_id],
        large_person_group_id=LARGE_PERSON_GROUP_ID
    )
    if not identify_results:
        return (False, None)

    result = identify_results[0]
    if not result.candidates:
        return (False, None)

    top_candidate = result.candidates[0]
    if top_candidate.confidence >= confidence_threshold:
        return (True, top_candidate.person_id)
    return (False, None)

def open_door_simulation(person_name):
    """
    In a real system, trigger door unlock. 
    Here, just print a message for demonstration.
    """
    print(f"[ACCESS GRANTED] Welcome, {person_name}! The door is now unlocked.")

def deny_access_simulation():
    """Simulate denying access."""
    print("[ACCESS DENIED] Unrecognized or unauthorized individual.")

def main():
    print("Door Access Control (Local Photo Upload)")

    # Create clients
    face_client = FaceClient(
        endpoint=FACE_ENDPOINT,
        credential=AzureKeyCredential(FACE_API_KEY)
    )
    face_admin_client = FaceAdministrationClient(
        endpoint=FACE_ENDPOINT,
        credential=AzureKeyCredential(FACE_API_KEY)
    )

    # Provide path to the image you want to check
    image_path = "path/to/test_image.jpg"
    if not os.path.exists(image_path):
        print(f"Error: File not found => {image_path}")
        return

    with open(image_path, "rb") as f:
        img_bytes = f.read()

    try:
        # DETECT face
        detected_faces = face_client.detect(
            image=img_bytes,
            detection_model=FaceDetectionModel.DETECTION03,
            recognition_model=FaceRecognitionModel.RECOGNITION04,
            return_face_id=True,
            return_face_attributes=[FaceAttributeTypeRecognition04.QUALITY_FOR_RECOGNITION]
        )

        if not detected_faces:
            print("No faces detected in the image. Access denied.")
            return

        # For simplicity, just use the first face
        face = detected_faces[0]
        if face.face_attributes.quality_for_recognition == QualityForRecognition.LOW:
            print("Face quality too low for recognition. Access denied.")
            return

        recognized, person_id = check_access(face_client, face.face_id)

        if recognized and person_id:
            # Retrieve the person's details to say hello by name
            person_info = face_admin_client.large_person_group.get_person(
                large_person_group_id=LARGE_PERSON_GROUP_ID,
                person_id=person_id
            )
            # Greet by name
            open_door_simulation(person_info.name)
        else:
            deny_access_simulation()

    except Exception as e:
        print(f"Error: {e}")

if __name__ == "__main__":
    main()

How It Works

Detection includes both MASK and QUALITY_FOR_RECOGNITION attributes.
We ensure nose_and_mouth_covered == True before proceeding to recognition.
If recognized (confidence >= 0.7), we print a success message. Otherwise, we deny attendance.

Real-Time Webcam Detection

For continuous detection (like an entry gate or reception area), you can capture frames from your webcam using OpenCV, then call Azure Face to check mask and recognition in real time.

File Name: realtime_webcam_detection.py

import os
import cv2
import time
from azure.core.credentials import AzureKeyCredential
from azure.ai.vision.face import FaceClient
from azure.ai.vision.face.models import (
    FaceDetectionModel,
    FaceRecognitionModel,
    FaceAttributeTypeDetection03,
    FaceAttributeTypeRecognition04,
    QualityForRecognition
)

# Replace with your actual values or environment variables
FACE_API_KEY = os.environ.get("FACE_APIKEY") or "<YourFaceAPIKey>"
FACE_ENDPOINT = os.environ.get("FACE_ENDPOINT") or "<YourFaceAPIEndpoint>"

# Must match your trained Large Person Group
LARGE_PERSON_GROUP_ID = "my-lpg-local-upload"

def identify_person(face_client, face_id, threshold=0.7):
    """Identify the face in the LPG. Returns (True, person_id) if recognized."""
    results = face_client.identify_from_large_person_group(
        face_ids=[face_id],
        large_person_group_id=LARGE_PERSON_GROUP_ID
    )
    if not results or not results[0].candidates:
        return (False, None)

    candidate = results[0].candidates[0]
    return (candidate.confidence >= threshold, candidate.person_id)

def main():
    print("[INFO] Starting Real-Time Face Mask & Recognition Detection")

    # Create FaceClient
    face_client = FaceClient(
        endpoint=FACE_ENDPOINT,
        credential=AzureKeyCredential(FACE_API_KEY)
    )

    # Open the default webcam
    cap = cv2.VideoCapture(0)
    if not cap.isOpened():
        print("[ERROR] Could not open webcam.")
        return

    try:
        while True:
            ret, frame = cap.read()
            if not ret:
                print("[ERROR] Failed to read frame from webcam.")
                break

            # Encode current frame to JPEG
            _, img_encoded = cv2.imencode('.jpg', frame)
            img_bytes = img_encoded.tobytes()

            # Detect faces
            try:
                faces = face_client.detect(
                    image=img_bytes,
                    detection_model=FaceDetectionModel.DETECTION03,
                    recognition_model=FaceRecognitionModel.RECOGNITION04,
                    return_face_id=True,
                    return_face_attributes=[
                        FaceAttributeTypeDetection03.MASK,
                        FaceAttributeTypeRecognition04.QUALITY_FOR_RECOGNITION
                    ]
                )
            except Exception as e:
                print(f"[ERROR] Azure Face detection error: {e}")
                continue

            # For each face, check mask & recognition
            for face in faces:
                # Draw bounding box
                rect = face.face_rectangle
                top, left, width, height = rect.top, rect.left, rect.width, rect.height
                cv2.rectangle(frame, (left, top), (left + width, top + height), (0, 255, 0), 2)

                # Default label
                msg = "No Mask Detected"

                # Check mask coverage
                mask_data = face.face_attributes.mask
                if mask_data and mask_data.nose_and_mouth_covered:
                    # Check face quality
                    if face.face_attributes.quality_for_recognition != QualityForRecognition.LOW:
                        recognized, person_id = identify_person(face_client, face.face_id)
                        if recognized:
                            msg = "Recognized + Masked"
                            # If you want the person's actual name:
                            # (You’d need the FaceAdministrationClient here)
                            # person_info = face_admin_client.large_person_group.get_person(
                            #     LARGE_PERSON_GROUP_ID, person_id
                            # )
                            # msg = f"{person_info.name} + Masked"
                        else:
                            msg = "Unknown, Masked"
                    else:
                        msg = "Low Quality Face"
                else:
                    msg = "No/Improper Mask"

                # Put text on frame
                cv2.putText(frame, msg, (left, top - 10), cv2.FONT_HERSHEY_SIMPLEX, 
                            0.5, (0, 255, 255), 2)

            # Show the result
            cv2.imshow('Real-Time Mask & Recognition', frame)

            # Press 'q' to quit
            if cv2.waitKey(1) & 0xFF == ord('q'):
                break

            time.sleep(0.3)  # Slight delay to reduce API calls

    except KeyboardInterrupt:
        print("[INFO] Interrupted by user.")
    finally:
        cap.release()
        cv2.destroyAllWindows()

if __name__ == "__main__":
    main()

How It Works

OpenCV obtains frames from the webcam.
Azure Face detection includes mask and quality attributes.
Bounding Boxes: We draw rectangles and text in the video.
If the face is recognized and masked, we label them "Recognized + Masked". Otherwise, we label them "Unknown, Masked" or "No/Improper Mask".

Integrating with an Attendance Database

In production, you would:

Log recognized individuals in a database with timestamps (e.g., SQL, Cosmos DB).
Trigger access control systems if recognized and masked.
Handle notifications or custom logic if an unrecognized or unmasked person is detected.

Troubleshooting & Best Practices

Confidence Threshold
Adjust 0.7 to balance false accepts vs. false rejects. In higher-security scenarios, go higher (e.g., 0.8).

Low-Light Conditions
Poor lighting may lower face detection accuracy and quality. Provide good ambient light or an IR camera in low-light environments.

API Rate Limits
The free tier has certain call-per-minute limits. If you’re capturing 30 frames per second, you’ll exceed it quickly!
Use throttling (time.sleep(...)) or a higher service tier.

Privacy & Security
Face recognition is subject to data protection laws (GDPR, HIPAA, etc.).
Store face data securely and keep your API Key safe (e.g., Azure Key Vault).

Region Support for Mask
Check the Azure docs to ensure your region supports the mask feature.
Conclusion

By combining Azure AI Face and OpenCV, you can build a robust attendance system that verifies both identity and mask compliance. This meets modern health guidelines, is touchless, and can be extended to real-time logging or controlling physical access points.

Next Steps:

Improve multi-person handling, logging, and auditing.
Integrate with hardware for an actual door lock system or turnstile.
Explore offline or on-edge solutions if you require higher throughput or internet-independent operation.

Happy Building!

Predict NYC Taxi Fares | End-to-end Azure MLOps with GitHub Actions

Vivek0712 — Fri, 14 Feb 2025 14:54:35 +0000

Implementation: https://github.com/Vivek0712/mlops-v2-gha-demo

AI workload operations revolve around curation of data and consumption of that data. Operations ensure that you maintain quality, reliability, security, ethics, and other standards you prioritize for your AI/ML workloads. In this blog, you’ll learn how to set up an end-to-end MLOps pipeline on Azure using GitHub Actions to predict NYC taxi fares with a linear regression model. This project follows industry best practices like DevOps, DataOps, MLOps, and GenAIOps.

Introduction to AI Workloads and Operational Methodologies
Understanding GitHub Actions
Why Azure Machine Learning with GitHub Actions?
Prerequisites
Set Up Authentication with Azure and GitHub Actions
- Create a Service Principal
- Assign Permissions to the Service Principal
Set Up the GitHub Repo
Deploy the Machine Learning Project Infrastructure with GitHub Actions
Next Steps
References

Introduction to AI Workloads and Operational Methodologies

AI workloads can be broken down into three main categories:

Application Development
Data Handling
AI Model Management

Different operational methodologies come into play across these categories:

DevOps focuses on managing the application development lifecycle with automated CI/CD pipelines and monitoring.
DataOps extends DevOps to handle data extraction, transformation, and loading (ETL/ELT) processes. It monitors data flow, data cleansing, and anomaly detection.
MLOps operationalizes machine learning workflows, handling model training, validation, and deployment pipelines.
GenAIOps, a specialized form of MLOps, targets generative AI solutions, focusing on model discovery, refining pretrained models, and handling unstructured or semi-structured data.

These methodologies often overlap. For instance, in discriminative AI, DataOps plays a larger role, while in generative AI, DevOps might be more heavily utilized due to more complex application development pipelines.

Understanding GitHub Actions

GitHub Actions is a CI/CD platform that can automate software development workflows. It allows you to:

Build and test every pull request or commit.
Deploy merged pull requests to production.
Automate tasks on other repository events (e.g., add labels to issues on creation).

GitHub Actions provides Linux, Windows, and macOS runners, or you can use self-hosted runners. This flexibility ensures that you can tailor your workflows to your specific infrastructure needs.

Why Azure Machine Learning with GitHub Actions?

Azure Machine Learning integrates seamlessly with GitHub Actions to automate various stages of the machine learning lifecycle, such as:

Deploying infrastructure for ML (compute, storage, networking).
Performing data preparation (ETL/ELT).
Training ML models at scale.
Deploying trained models as real-time endpoints or batch endpoints.
Monitoring deployed models to track performance and detect anomalies.

In this blog, we’ll build an end-to-end MLOps pipeline to train a linear regression model to predict NYC taxi fares. We’ll be using the recommended Azure architecture for MLOps and the Azure MLOps (v2) solution accelerator to simplify the process.

Prerequisites

Azure Subscription
- If you don’t have one, you can create a free Azure account.
Azure Machine Learning Workspace
- Create a workspace.

Git installed locally.
A GitHub repository or a GitHub organization where you can create or fork repositories.
Basic understanding of Azure Machine Learning concepts like workspaces, compute, and endpoints.

Set Up Authentication with Azure and GitHub Actions

Before setting up an MLOps project using Azure Machine Learning, you need to configure secure authentication between GitHub Actions and Azure.

Create a Service Principal

Go to Azure Portal > Azure Active Directory > App registrations.
Select New Registration.

Configure:
- Name: Vivek-azureML-GHdemo (or any name you prefer)
- Supported account types: Accounts in any organizational directory (Any Microsoft Entra directory - Multitenant)
After creating the app registration, go to Certificates & Secrets and:

Select New client secret.
Copy the secret value and store it in a safe place (this value will be used in GitHub secrets).

Copy the Application (client) ID, Directory (tenant) ID, and Subscription ID for the next steps.

Important: Keep your secret value secure. You will not be able to view it again once you leave this page.

Assign Permissions to the Service Principal

Navigate to your Azure Subscription.
Go to Access Control (IAM).

Select +Add and choose Add role assignment.
Assign the Contributor role to your new Service Principal (Vivek-azureML-GHdemo) so that it has the necessary permissions to create and manage resources.

Set Up the GitHub Repo

We’ll use the MLOps v2 Demo Template Repo as our starting point:

Fork the repository
- Go to https://github.com/Azure/mlops-v2-gha-demo/fork to create a fork in your own GitHub org.

Configure secrets in your forked repository:

From your GitHub repo, go to Settings > Secrets > Actions.
Select New repository secret.
Set the following secrets using the corresponding values from your Azure Service Principal:
- AZURE_CREDENTIALS (Paste the full JSON output you obtained when creating the SP)
- ARM_CLIENT_ID
- ARM_CLIENT_SECRET
- ARM_SUBSCRIPTION_ID
- ARM_TENANT_ID

Here’s an example of what your AZURE_CREDENTIALS secret might look like (JSON snippet):

   {
     "clientId": "<YOUR_SERVICE_PRINCIPAL_APP_ID>",
     "clientSecret": "<YOUR_SERVICE_PRINCIPAL_SECRET>",
     "subscriptionId": "<YOUR_AZURE_SUBSCRIPTION_ID>",
     "tenantId": "<YOUR_AZURE_TENANT_ID>",
     "activeDirectoryEndpointUrl": "https://login.microsoftonline.com",
     "resourceManagerEndpointUrl": "https://management.azure.com/",
     "activeDirectoryGraphResourceId": "https://graph.windows.net/",
     "sqlManagementEndpointUrl": "https://management.core.windows.net:8443/",
     "galleryEndpointUrl": "https://gallery.azure.com/",
     "managementEndpointUrl": "https://management.core.windows.net/"
   }

Deploy the Machine Learning Project Infrastructure with GitHub Actions

Once your secrets are in place, you can use the Azure MLOps (v2) solution accelerator code in the forked repository to deploy the required Azure Machine Learning resources (e.g., workspace, compute, data stores, etc.).

In your forked repo, go to Actions.
Find the workflow that corresponds to infrastructure deployment—usually named something like .github/workflows/infra-deploy.yml.
Manually run the workflow or wait for the next push/merge to trigger it automatically.
Monitor the workflow’s progress on the Actions tab.
Once completed, verify the resources in the Azure portal:
- Azure Machine Learning Workspace
- Any associated resource group and compute resources

Note: If you need more specific instructions (for example, customizing the workflow), refer to the official documentation:

Deploy machine learning project infrastructure with GitHub Actions

Next Steps

After your infrastructure is deployed, you can proceed to:

Configure Data Preparation
- Use your new Azure Machine Learning workspace to set up data ingestion and data versioning (e.g., using Azure Blob Storage or Azure Data Lake).
Train and Evaluate the Model
- Commit your training scripts (e.g., a linear regression training script) to the GitHub repo.
- Update your GitHub Actions workflow to automatically trigger model training and evaluation upon code updates.
Model Deployment
- Deploy the trained model to an Azure Container Instance or Azure Kubernetes Service for real-time inference.
Monitor Your Model
- Set up alerts and performance monitoring for your deployed endpoints.
- Integrate logs, metrics, and triggers for data drift detection or model re-training.

For a full walkthrough of these steps, follow the official documentation:

Set up an MLOps project with GitHub Actions

References

Congratulations! You’ve set up an end-to-end MLOps pipeline using Azure Machine Learning and GitHub Actions to predict NYC taxi fares. With these best practices, you can continuously integrate and deploy changes to your ML models, ensuring reliability and scalability in your AI solutions.

Happy MLOps!

Build a Production LLM-Powered QC Visual Inspection System Using Azure AI Foundry

Vivek0712 — Thu, 13 Feb 2025 15:31:53 +0000

In many manufacturing and quality-control (QC) processes, visual inspection is a critical step. However, building an automated inspection system has traditionally required specialized machine vision tools and complex integrations. With Azure AI Foundry and its Prompt Flow feature, you can now orchestrate powerful large language models (LLMs)—like GPT-4 with Vision—to handle sophisticated visual inspection tasks with minimal friction.

In this post, we’ll walk through how to set up a new project in Azure AI Foundry, clone a ready-made quality-inspection sample, and then review how you can customize and deploy it for your own production needs.

1. Sign in to Azure AI Foundry

Head to the Azure AI Foundry portal (provided by your Azure environment).
Sign in using your Azure account credentials.
You’ll land on the Home page of Azure AI Foundry once authenticated.

2. Create a New Hub and Project

From the Home page, click + Create project.
Enter a Name for your project (for instance, “QC Visual Inspection”).
Choose an existing hub or click Create new hub to create one from scratch.
Give your new hub a memorable name (e.g., “ProductionQC”), then select Create.

Now your Azure AI Foundry workspace has a hub (which is like a container or environment) and a project (your application context).

3. Introduction to Prompt Flow

Prompt Flow is a development tool in Azure AI Foundry that helps you orchestrate LLMs, prompts, and Python tools in a visual, node-based interface. It speeds up the cycle of prototyping, experimenting, iterating, and deploying AI applications that rely on LLMs.

With Prompt Flow, you can:

Orchestrate: Combine Large Language Models, Python scripts, and prompts into a reproducible pipeline.
Visualize: Build flows via a drag-and-drop style interface—no heavy coding required.
Experiment & Iterate: Quickly test variations of prompts, compare outputs, and optimize your AI solution.

4. Clone the Quality Inspection Sample

Under Prompt flow in the Azure AI Foundry portal, you will see sample flows you can clone. For a visual inspection use case, the Detect Defects sample is an excellent starting point:

Find the Detect Defects tile (as shown in the “Create a new flow” screenshot).
Click Clone to create your own copy.
Name the flow (e.g., “QC_VisualInspection_Flow”).

5. Dive into the Flow Details

Inside the Detect Defects flow, you’ll see several key components:

Inputs:

reference_image: The “perfect” or expected product image.

test_image: The image of the item to be inspected.

system_message: A text prompt that instructs the LLM (e.g., “You’re a professional defect detector. Your job is to compare…”).

Resize Image (Python):

This step ensures the reference and test images are standardized in size/format before they’re fed into the LLM.

GPT4V_AOAI Node:

This node calls GPT-4 with Vision capabilities in Azure OpenAI to analyze whether the two images match. The model is instructed to respond with “No defect detected” or “Defect detected” along with a detailed reason.

Outputs:

The final result from GPT-4 is captured in the response field.

6. Testing & Iterating

Once your flow is cloned and configured:

Upload or drag-and-drop your reference_image and test_image.
Adjust your prompt in system_message if needed.
Click Start compute session (or Run once the session is ready).

View the logs and final output to see whether the system detected any defects.
If results aren’t accurate enough, you can:
- Fine-tune the prompt.
- Add additional Python logic in your flow to enhance the pre-processing or post-processing.

Prompt Flow makes it easy to iterate by adjusting nodes, re-running, and comparing performance.

7. Deployment Considerations

For a production QC system, you’ll likely want to:

Automate Input: Feed images from your production line into the flow automatically (e.g., from a camera or an upload process).
Scale: Use the Azure AI Foundry’s distributed compute capabilities if you have high throughput.
Monitor: Track performance metrics, run times, and defect-detection accuracy.
Integrate: Provide the resulting output to a database, a dashboard, or an alert system to ensure real-time responses to quality issues.

Conclusion

By following these steps, you’ll have a running LLM-powered QC Visual Inspection system in Azure AI Foundry:

Create a hub and project to organize your application.
Clone the Detect Defects flow (sample) to jumpstart your solution.
Customize prompts, inputs, or Python scripts to match your use case.
Test and iterate using Prompt Flow’s built-in debugging and evaluation features.
Deploy your final solution to production for real-time defect detection and automated QC.

Azure AI Foundry removes much of the complexity normally involved in building AI solutions—especially those requiring visual and language understanding. With GPT-4 (Vision) under the hood, you can quickly set up advanced visual inspections that would otherwise take a team of vision experts to perfect.

🚀 Happy building!

Analyse Customer Sentiment using Azure Data Factory pipeline

Vivek0712 — Wed, 12 Feb 2025 16:30:55 +0000

This guide demonstrates how to:

Copy Data from Azure Blob Storage to a sink.
Transform (preprocess) the data via an Azure Data Factory Data Flow.
Invoke an Azure ML pipeline to run an inference script (using the AzureMLExecutePipeline activity).

Prerequisites

Azure Subscription
- You need an active Azure subscription.
Azure Data Factory (V2) instance
Azure Blob Storage (with data to be processed)
Azure Machine Learning Workspace
- A published ML pipeline that can be triggered
Sufficient Permissions
- Admin or Contributor on the above resources

High-Level Flow

Linked Service in ADF to Azure Blob Storage
Datasets (input and output)
Copy Data Activity (Blob -> Blob or other sink)
Mapping Data Flow (for data preprocessing)
AzureMLExecutePipeline (to run inference in Azure ML)
Inference Script in Azure ML pipeline (e.g., scoring with a trained model)

1. Create a Linked Service (Blob Storage)

In Azure Data Factory, navigate to Manage > Linked services and create a new linked service for your Blob Storage. Below is an example JSON (if you prefer the code view). Store your connection string or SAS token securely (e.g., in Key Vault).

{
  "name": "LS_AzureBlobStorage",
  "properties": {
    "type": "AzureBlobStorage",
    "typeProperties": {
      "connectionString": "@pipeline().parameters.blobConnectionString"
    }
  }
}

Note:

In production, reference your connectionString from Azure Key Vault rather than hardcoding credentials in ADF.

2. Create Datasets

2.1 Dataset for Input (Blob)

{
  "name": "DS_BlobInput",
  "properties": {
    "linkedServiceName": {
      "referenceName": "LS_AzureBlobStorage",
      "type": "LinkedServiceReference"
    },
    "type": "Blob",
    "typeProperties": {
      "folderPath": "input-data",
      "fileName": "mydata.csv"
    },
    "schema": []
  }
}

If you need to handle dynamic folder paths or filenames, parameterize folderPath and fileName.

2.2 Dataset for Output (Blob)

{
  "name": "DS_BlobOutput",
  "properties": {
    "linkedServiceName": {
      "referenceName": "LS_AzureBlobStorage",
      "type": "LinkedServiceReference"
    },
    "type": "Blob",
    "typeProperties": {
      "folderPath": "output-data",
      "fileName": "mytransformeddata.csv"
    },
    "schema": []
  }
}

3. Create the ADF Pipeline

We will define a pipeline that contains three activities:

Copy Data from Blob to Blob (or another sink)
Data Flow to transform the data
Azure ML Execute Pipeline to perform inference

Below is an example pipeline JSON named PL_CopyTransformAML.json:

{
  "name": "PL_CopyTransformAML",
  "properties": {
    "activities": [
      {
        "name": "CopyFromBlob",
        "type": "Copy",
        "dependsOn": [],
        "policy": {
          "timeout": "7.00:00:00",
          "retry": 0,
          "retryIntervalInSeconds": 30,
          "secureOutput": false,
          "secureInput": false
        },
        "userProperties": [],
        "typeProperties": {
          "source": {
            "type": "BlobSource"
          },
          "sink": {
            "type": "BlobSink"
          },
          "enableStaging": false,
          "dataIntegrationUnits": 4
        },
        "inputs": [
          {
            "referenceName": "DS_BlobInput",
            "type": "DatasetReference"
          }
        ],
        "outputs": [
          {
            "referenceName": "DS_BlobOutput",
            "type": "DatasetReference"
          }
        ]
      },
      {
        "name": "TransformDataFlow",
        "type": "DataFlow",
        "dependsOn": [
          {
            "activity": "CopyFromBlob",
            "dependencyConditions": [
              "Succeeded"
            ]
          }
        ],
        "policy": {
          "timeout": "7.00:00:00",
          "retry": 0,
          "retryIntervalInSeconds": 30,
          "secureOutput": false,
          "secureInput": false
        },
        "typeProperties": {
          "dataflow": {
            "referenceName": "DF_PreprocessingFlow",
            "type": "DataFlowReference"
          },
          "staging": {
            "linkedServiceName": {
              "referenceName": "LS_AzureBlobStorage",
              "type": "LinkedServiceReference"
            },
            "folderPath": "temp/staging"
          }
        }
      },
      {
        "name": "RunAzureMLPipeline",
        "type": "AzureMLExecutePipeline",
        "dependsOn": [
          {
            "activity": "TransformDataFlow",
            "dependencyConditions": [
              "Succeeded"
            ]
          }
        ],
        "policy": {
          "timeout": "7.00:00:00",
          "retry": 0,
          "retryIntervalInSeconds": 30,
          "secureOutput": false,
          "secureInput": false
        },
        "typeProperties": {
          "mlPipelineId": "@pipeline().parameters.mlPipelineId",
          "mlWorkspaceLinkedServiceName": {
            "referenceName": "LS_AzureMLWorkspace",
            "type": "LinkedServiceReference"
          },
          "experimentName": "InferenceExperiment",
          "pipelineParameters": {
            "input_data_path": "@concat('https://<yourstorageaccount>.blob.core.windows.net/output-data/mytransformeddata.csv')",
            "output_data_path": "@concat('https://<yourstorageaccount>.blob.core.windows.net/output-data/predictions.csv')"
          }
        }
      }
    ],
    "parameters": {
      "mlPipelineId": {
        "type": "String",
        "defaultValue": "/subscriptions/xxxxxxxx-xxxx-xxxx-xxxx-xxxxxxxxxxxx/resourceGroups/MyRG/providers/Microsoft.MachineLearningServices/workspaces/MyAMLWorkspace/Pipelines/my-ml-pipeline-id"
      },
      "blobConnectionString": {
        "type": "SecureString"
      }
    }
  }
}

Key Points

CopyFromBlob copies data from the DS_BlobInput dataset to DS_BlobOutput.
TransformDataFlow references a data flow named DF_PreprocessingFlow.
RunAzureMLPipeline uses the AzureMLExecutePipeline activity to trigger a published pipeline in Azure ML.

4. Define the Data Flow (Preprocessing)

Below is an example Mapping Data Flow definition (DF_PreprocessingFlow.json) that filters out rows with null values in certain columns. It reads from DS_BlobOutput (as input) and writes back to DS_BlobOutput (or a different dataset).

{
  "name": "DF_PreprocessingFlow",
  "properties": {
    "type": "MappingDataFlow",
    "typeProperties": {
      "sources": [
        {
          "name": "source1",
          "dataset": {
            "referenceName": "DS_BlobOutput",
            "type": "DatasetReference"
          },
          "streamName": "inputStream"
        }
      ],
      "sinks": [
        {
          "name": "sink1",
          "dataset": {
            "referenceName": "DS_BlobOutput",
            "type": "DatasetReference"
          },
          "streamName": "outputStream"
        }
      ],
      "transformations": [
        {
          "name": "FilterNulls",
          "type": "Filter",
          "streamName": "inputStream",
          "conditions": {
            "condition": "isNull(columnA) == false && isNull(columnB) == false"
          }
        }
      ]
    }
  }
}

In a real-world scenario, your transformations could be more complex (e.g., derived columns, joins, aggregations, etc.).

5. Azure ML Linked Service in ADF

Create a Linked Service to your Azure Machine Learning workspace:

{
  "name": "LS_AzureMLWorkspace",
  "properties": {
    "type": "AzureMLService",
    "typeProperties": {
      "subscriptionId": "xxxxxxxx-xxxx-xxxx-xxxx-xxxxxxxxxxxx",
      "resourceGroupName": "MyRG",
      "mlWorkspaceName": "MyAMLWorkspace",
      "servicePrincipalId": "@pipeline().parameters.servicePrincipalId",
      "servicePrincipalKey": "@pipeline().parameters.servicePrincipalKey",
      "tenantId": "@pipeline().parameters.tenantId"
    }
  }
}

Authentication: Example uses Service Principal credentials. You can leverage Managed Identity if configured.

6. Azure ML Pipeline & Inference Script

6.1 Creating/Publishing an Azure ML Pipeline

Use the Azure ML Python SDK (or CLI v2) in a notebook or script to define, validate, and publish the pipeline:

# azure_ml_pipeline.py
from azureml.core import Workspace, Environment, Datastore
from azureml.core.runconfig import RunConfiguration
from azureml.pipeline.core import Pipeline
from azureml.pipeline.steps import PythonScriptStep
from azureml.pipeline.core import PipelineParameter

# Connect to AML Workspace
ws = Workspace.from_config()

# Create environment
env = Environment(name="inference-env")
env.python.conda_dependencies.add_pip_package("pandas")
env.python.conda_dependencies.add_pip_package("scikit-learn")

run_config = RunConfiguration()
run_config.environment = env

# Pipeline parameters
input_data_path_param = PipelineParameter(name="input_data_path", default_value="input_path_placeholder")
output_data_path_param = PipelineParameter(name="output_data_path", default_value="output_path_placeholder")

# Create a PythonScriptStep
inference_step = PythonScriptStep(
    name="InferenceStep",
    script_name="score.py",
    arguments=[
        "--input_data", input_data_path_param,
        "--output_data", output_data_path_param
    ],
    compute_target="cpu-cluster",  # AML compute name
    source_directory="./scripts",  # folder containing score.py
    runconfig=run_config
)

# Build pipeline
pipeline = Pipeline(workspace=ws, steps=[inference_step])
pipeline.validate()

# Publish pipeline
published_pipeline = pipeline.publish(name="InferencePipeline")
print("Published pipeline ID: ", published_pipeline.id)

Once published, you’ll receive a Pipeline ID (used in the ADF mlPipelineId parameter).

6.2 Example Inference Script (`score.py`)

A simple inference script using a scikit-learn model (model.pkl):

# score.py
import argparse
import pandas as pd
import joblib

def main():
    parser = argparse.ArgumentParser()
    parser.add_argument("--input_data", type=str, required=True)
    parser.add_argument("--output_data", type=str, required=True)
    args = parser.parse_args()

    # Read data (assuming CSV from a publicly accessible URL or local path)
    df = pd.read_csv(args.input_data)

    # Load model
    model = joblib.load("model.pkl")

    # Predict
    predictions = model.predict(df)

    # Convert predictions to DataFrame
    pred_df = pd.DataFrame(predictions, columns=["prediction"])

    # Write predictions to CSV
    pred_df.to_csv(args.output_data, index=False)
    print("Inference complete. Predictions saved.")

if __name__ == "__main__":
    main()

7. Putting It All Together

Upload/Deploy your pipeline JSON, data flow JSON, dataset JSON, and linked service JSON to your ADF.
Publish your ADF changes.
Trigger the pipeline (PL_CopyTransformAML).

Pipeline Flow Recap

CopyFromBlob pulls data from DS_BlobInput to DS_BlobOutput.
TransformDataFlow applies filtering (or other transformations) via DF_PreprocessingFlow.
RunAzureMLPipeline invokes the published Azure ML pipeline, passing the processed data path as input for inference.

7.1 Run & Monitor

In the ADF Author pane, select your pipeline.
Debug or Trigger Now.
Track the status in the Monitor pane.

Conclusion

With these steps, you've built a fully orchestrated solution:

Data ingestion from Blob Storage
Data preprocessing in ADF Data Flows
Inference using an Azure ML pipeline and script

You can extend or modify these steps for more complex transformations, multiple models, or advanced parameterization. Always remember to keep secrets secure (via Key Vault) and optimize your compute usage in both ADF and AML.

Happy Building!

Electronics-Datasheet-GPT: A Step-by-Step Guide to Building Your Own Datasheet Q&A AI Agent

Vivek0712 — Tue, 11 Feb 2025 14:50:50 +0000

Electronics-Datasheet-GPT: A Step-by-Step Guide to Building Your Own Datasheet Q&A Azure AI Agent

Are you tired of digging through endless PDF datasheets and scrolling past pages of technical jargon just to find a single spec? Meet Electronics-Datasheet-GPT—an Azure AI Agent that can answer your hardware and electronics questions directly from product datasheets. In this blog, we’ll walk through the exact steps to create and deploy your own agent in Azure AI Foundry, using a basic setup.

Why Build an Electronics-Datasheet-GPT?

Whether you’re tinkering with microcontrollers like Arduino or working on advanced PCB designs, quick and accurate access to component specifications is essential. Instead of searching manually for that elusive voltage rating or I/O pin number, an AI Agent can:

Instantly Search and Retrieve key info from thousands of lines of datasheets.
Reduce Mistakes by delivering exact technical data on demand.
Improve Productivity—spend more time building, less time reading.

And the best part? You don’t need to spin up a bunch of complicated resources or code it from scratch—Azure AI Foundry handles the heavy lifting.

1. Prerequisites

Azure Subscription
- If you don’t already have one, sign up for a free Azure account.
Azure AI Developer Role
- Ensure you have the Azure AI Developer RBAC role assigned. This permission is necessary to create AI Agents and deploy models.

Basic Familiarity with Azure AI Foundry
- We’ll be using the Azure AI Foundry portal, which supports a basic agent setup.

2. Basic vs. Standard Setup

Azure AI Foundry offers two agent deployment models:

Basic Setup: Quick, fully-managed approach. Azure handles all the storage and search resources for you.
Standard Setup: You manage (and pay for) your own single-tenant search and storage. More control, but more complexity.

Since basic setup is supported directly in the Azure AI Foundry portal, that’s the easiest path to start building your Electronics-Datasheet-GPT.

3. Create a Hub and Project in Azure AI Foundry

Sign In to Azure AI Foundry Head to the Azure AI Foundry portal and sign in with your Azure account.

Create a New Project
- Click + Create project on the Home page.
- Give your project a Name (e.g., “Electronics-Datasheet-GPT-Project”).

Choose an existing hub or Create new hub and name it.

Select Create.

Your new project is now ready to host your custom AI Agent.

4. Deploy a Model (Pick Your Brain: `gpt-4o`, `gpt-4`, etc.)

In the Azure AI Foundry portal, open your newly created (or existing) project.
From the Project Overview, click Agents.

Select the Azure OpenAI Service Resource

If no model is currently deployed, select Deploy a model. You’ll be presented with a list of available LLMs (e.g., gpt-4o, gpt-4, gpt-35-turbo).
Choose the model that suits your performance and cost needs. For instance, gpt-4o is a great choice for robust Q&A.

Click Confirm to spin up the deployment.

This step essentially sets up the core “brain” that your agent will use to interpret and answer queries.

5. Create Your Electronics-Datasheet-GPT Agent

Go to the Agents section and select New agent.
Give your agent a Name—for instance, “Electronics-Datasheet-GPT.”
Under Instructions, add a clear directive for your agent. For example:

Your task is to analyze any user query and reference the attached datasheet knowledge base (Arduino datasheet, etc.). If the question is outside this scope, respond with “This is out of scope.”

This clarifies your agent’s primary job: to help with datasheet queries, nothing else.

6. Attach the Datasheet Knowledge

Now you’ll give your agent the data it needs to answer questions—i.e., your Arduino or other electronics datasheets.

In the Setup pane (usually on the right):
Scroll to Knowledge.
Click Add to attach your existing knowledge store or upload a new one.
If you have a preprocessed PDF or document with the datasheet info, select that to add it to the agent’s knowledge base.

Once uploaded, your agent will now be able to search this data to find relevant answers.

7. (Optional) Add Tools and Actions

If you want your agent to do more than just Q&A—like run code snippets or scrape the web—scroll down to Actions in the Setup pane and select Add. However, for a straightforward datasheet Q&A, you may not need additional actions or tools.

8. Test Your Agent in the Playground

Here’s the fun part—seeing your Electronics-Datasheet-GPT in action!

Open the Agent Playground: In the Create and debug your agents screen, select your new agent.

You’ll see a chat interface. Ask your first question, for example:

What is the recommended operating voltage for Arduino Uno?
What is the function of PIN 11, and P15

The agent will comb through the datasheet knowledge base and respond with the correct voltage range (usually 7-12V recommended, 5V operating).
If a user question is irrelevant or not found in the datasheet, the agent should respond:

This is out of scope.

9. Fine-Tune and Refine

Edit Agent Instructions: Tweak the instructions if you notice the agent drifting off-topic or not providing enough detail.
Add More Datasheets: If your project evolves to include more components, simply upload additional PDFs or documents to your knowledge store.
Enable or Disable Tools: As needs grow, you might incorporate code execution or web search to further empower your agent.

10. Wrap-Up and Next Steps

Congratulations—you’ve built Electronics-Datasheet-GPT! You now have an AI Agent that can handle your electronics queries quickly by referencing the technical documentation you provided.

Where to go from here?

Deploy to Production: Integrate your agent into a web or mobile app through the provided endpoints.
Monitor and Optimize: Keep an eye on costs and usage.
Explore Standard Setup: If you outgrow the basic approach, consider a standard setup for more control over resources.

Conclusion

No more scrolling through multi-page PDFs to find that tiny detail—your new Azure AI Agent has you covered. By following these steps, you’ve harnessed Azure AI Foundry’s powerful tools in a basic, fully-managed setup. It’s never been easier to get an AI up and running for specialized tasks like datasheet Q&A.

Try it out yourself, and enjoy watching your agent streamline your electronics research and build process. And if you’re feeling adventurous, add more data sources, tools, or even transition to a standard setup. The possibilities are endless with Electronics-Datasheet-GPT on Azure!

Experimenting with DeepSeek-R1 Model in VS Code Hosted via GitHub Models

Vivek0712 — Mon, 10 Feb 2025 13:52:22 +0000

Experimenting with DeepSeek-R1 Model in VS Code Using GitHub Models Platform

Introduction

AI-assisted development is revolutionizing how developers write and test code. GitHub Models provides a range of AI-powered tools that enhance workflows, including DeepSeek-R1, a text generation model. By integrating this model into Visual Studio Code (VS Code) using the AI Toolkit, developers can streamline coding tasks and improve productivity.

If you want to develop a generative AI application, you can use GitHub Models to find and experiment with AI models for free. Once you are ready to bring your application to production, you can switch to a token from a paid Azure account.

This guide outlines the setup process, model selection, and how to use DeepSeek-R1 effectively within VS Code.

Step 1: Installing the AI Toolkit in VS Code

The AI Toolkit for Visual Studio Code allows seamless interaction with generative AI models. To install it:

Open VS Code and navigate to the Extensions Marketplace.
Search for "AI Toolkit for Visual Studio Code".
Click Install to add the extension.
Once installed, click on the AI Toolkit icon in the Activity Bar to access the toolkit.

Step 2: Exploring the Model Catalog

Once authorized, you can browse and select models from the Model Catalog:

Open AI Toolkit and click Open Model Catalog.
Browse through the list of available models.
Select DeepSeek-R1, a robust text generation model.
Click Try in Playground under the DeepSeek-R1 model.

Step 3: Authorizing the AI Toolkit with GitHub

To access AI models hosted on GitHub, you must authorize the AI Toolkit:

Click on AI Toolkit in the Activity Bar.
A prompt will appear requesting you to sign in using GitHub.
Click Allow to grant the necessary permissions.

Step 4: Using DeepSeek-R1 in the Playground

After selecting DeepSeek-R1, you can test it in the AI Playground:

Enter a prompt, such as "Write me a simple HTML code to say Hi, bloggers!".
Adjust inference parameters (e.g., response length) as needed.
Click Run to generate a response.

Step 5: Implementing the Generated Code in VS Code

With the generated HTML snippet from DeepSeek-R1, you can integrate it into your project:

Copy the generated HTML code.
Create a new file in VS Code (e.g., deepseek.html).
Paste the HTML code and save the file.
Open the file in a browser to preview the output.

Conclusion

The AI Toolkit for Visual Studio Code simplifies interactions with AI models, enabling developers to integrate AI-powered functionalities into their workflows. DeepSeek-R1 assists in generating code snippets, automating tasks, and boosting productivity.

Explore different models available in GitHub Models and see how AI can optimize your coding process.

Happy coding! 🚀

How AWS GenAI boosted my day to day Productivity

Vivek0712 — Fri, 31 May 2024 17:49:16 +0000

GenAI is everywhere these days, and it's no surprise why—it’s the tech world's latest obsession and has skyrocketed to the top of the Gartner hype cycle. But like any hyped technology, it comes with its share of pros and cons. On the plus side, it can automate mundane tasks, boost productivity, and spark creativity. On the downside, it raises questions about job displacement and ethical use. Despite the mixed feelings, one thing is clear: GenAI is here to stay, and it's transforming how we think about technology and its potential.

The agenda is fairly simple and packed with exciting highlights. First up, there's Amazon PartyRock for Newbies (or anyone!)—a fun and engaging way to get introduced to the world of GenAI.
Next, we dive into Amazon CodeWhisperer, a powerful tool for developers that offers real-time code suggestions and automation to streamline coding tasks.
Finally, we explore Amazon Q for Business and beyond, a tool that integrates with Amazon QuickSight to provide insightful data analysis and business intelligence, helping businesses make informed decisions quickly. Whether you're a beginner or a seasoned developer, there's something in this agenda to supercharge your productivity with GenAI on AWS.

Amazon PartyRock

Imagine diving into the world of generative AI without needing to write a single line of code. With Amazon PartyRock, learning generative AI fundamentals becomes an exciting and hands-on experience. PartyRock is a code-free app builder designed to make learning easy and fun. You can experiment with prompt engineering techniques, review generated responses, and develop a solid intuition for generative AI, all while creating and exploring entertaining apps.

At the heart of PartyRock is access to powerful foundation models from Amazon Bedrock. Amazon Bedrock is a fully managed service that provides foundation models (FMs) from Amazon and other leading AI companies, available right on AWS. It’s the simplest way to build and scale generative AI applications with these foundation models. With PartyRock, you get to tap into the full potential of these FMs in an intuitive, code-free app-building playground. This setup is perfect for learning the essentials of prompt engineering and generative AI, making it accessible and engaging for everyone, regardless of their coding background.

With Amazon PartyRock, you can revolutionize your daily routine by creating custom AI apps without writing a single line of code. Imagine a smart meal planner that suggests recipes based on your pantry, a personalized workout coach that designs fitness plans, or a daily task organizer that prioritizes your to-do list. You can even build a budget tracker to oversee your finances.

PartyRock makes it easy and fun to automate everyday tasks, enhancing your productivity and well-being with intuitive, AI-driven solutions. Dive in and start building your personalized AI apps today!

Also do check out one of the amazing project from AWS Community by Stephen Sennett

A Day in the Developer Life of Vivek

Today, I log into work and am greeted with a new task assignment: upload all 'CSV' data in the 'temp' folder to an S3 bucket named 'code-demo'. This involves writing a Python function to read the CSV files and upload them to the specified S3 bucket. It’s time to nail the task.

I choose my favorite IDE—VSCode (no doubt!). But sometimes, even with the best tools, you need a bit of extra help.

Like many developers, I head over to Stack Overflow to get the relevant code snippet. After some searching, I find a solution that fits my needs.

Here's the Python code I found and used:

import boto3
import os

def upload_to_s3(file, bucket, objname):
    s3 = boto3.resource('s3')
    bucket = s3.Bucket(bucket)
    bucket.Object(objname).put(Body=open(file, 'rb'))

def csv_files_from_folder(folderpath):
    path = folderpath
    table_list = []
    for filename in os.listdir(path):
        if filename.endswith('.csv'):
            table_list.append(path + "\\" + filename)
    return table_list

files = csv_files_from_folder("tempfolder")
for file in files:
    upload_to_s3(file, "codedemo", file)

This script iterates through all CSV files in the 'tempfolder', uploading each one to the 'codedemo' S3 bucket.

How Amazon CodeWhisperer Enhances My Productivity

Amazon CodeWhisperer is my ML-powered coding companion, and it has become an invaluable part of my development workflow. Here’s how it helps me become a more productive developer:

Real-time Code Recommendations: CodeWhisperer generates code recommendations based on my comments and method names. This feature saves me time by providing relevant suggestions as I type, reducing the need to search for solutions online.
Powered by Machine Learning: It leverages machine learning to offer customized code recommendations for all my Java, Python, and JavaScript projects, ensuring that I write efficient and effective code.
Responsibly Use AI: CodeWhisperer empowers me to use AI responsibly, helping me create syntactically correct and secure applications. This not only boosts my productivity but also enhances the quality of my code.

With Amazon CodeWhisperer integrated into my IDE, I can focus more on solving complex problems and less on repetitive coding tasks. It’s like having a knowledgeable co-pilot who guides me through the development process, making my coding experience smoother and more efficient.

How Amazon Q Enhances Business Productivity

Amazon Q is a game-changer for business productivity, providing a generative AI-powered assistant tailored to work seamlessly with your business needs. By leveraging the vast data and expertise available within your company's information repositories, code, and enterprise systems, Amazon Q offers fast, relevant answers to pressing questions, solves problems, generates content, and takes actions based on the data provided. Here's how Amazon Q can enhance productivity on the business side:

1. Amazon Q in VSCode Extension:
Amazon Q is available as part of the AWS Toolkit extension in VSCode, making it accessible directly within your development environment. This integration allows you to leverage Amazon Q's powerful capabilities without leaving your IDE, further enhancing your productivity and streamlining your workflow.

2. Enhanced Decision-Making:
Integrating with Amazon QuickSight, Amazon Q allows you to generate analysis and visualizations from simple prompts. This capability enables you to make data-driven decisions swiftly, reducing the time spent on data gathering and interpretation.

3. Integration with AWS Best Practices:
Amazon Q helps you adhere to AWS best practices, ensuring that your use of AWS services is optimized and secure. By offering guidance and recommendations on AWS usage, it helps maintain the integrity and efficiency of your cloud operations, reducing the risk of errors and enhancing overall productivity.

Example of Amazon Q in Action:

Imagine you're analyzing the difference between website unique visits and total paid conversions. With Amazon Q, you can simply type a natural language query, and it will generate a visual representation of the data, helping you quickly identify trends and insights without the need for complex queries or manual data manipulation.

Sample Query:
"Show the difference in number of website unique visits versus total paid conversions."

Result:
Amazon Q interprets the query and generates a visual analysis, providing a clear and concise view of the data, which you can then use to make informed business decisions.

GenAI is not here to take over your jobs but to supercharge your productivity! With tools like Amazon CodeWhisperer and Amazon Q, you can streamline your development workflow, make data-driven business decisions, and foster creativity and innovation. These powerful AI-driven solutions are designed to assist you, making complex tasks more manageable and freeing you to focus on what truly matters. Embrace the future of productivity with GenAI on AWS and watch your efficiency and effectiveness soar.

What If Amazon Prime A/B Tests IPL Broadcasting?

Vivek0712 — Wed, 21 Feb 2024 17:53:40 +0000

In a rapidly evolving digital marketplace, Amazon Prime seeks to solidify its leadership by critically analyzing competitors, understanding user preferences, and innovating its pricing and benefits structure. This exploration involves a detailed comparative analysis to identify competitive edges, thorough market research to capture current trends and user demands, and strategic recommendations for diversifying pricing tiers to better meet diverse consumer needs. Central to this strategy is the implementation of A/B testing, a methodical approach to refine and perfect the Prime experience. Through this multifaceted strategy, Amazon Prime aims to not only keep pace with market dynamics but to set new standards for customer satisfaction and engagement in the digital subscription space.

Views are my own

Shipping and Delivery Benefits

Amazon Prime revolutionizes the online shopping experience with exceptional shipping and delivery perks:

Free Delivery: Eligible addresses enjoy no shipping costs on Prime-eligible items.
Fast Delivery Options: Ranging from One-Day to Same-Day Delivery, ensuring swift arrivals.
No-Rush Shipping: Opt for slower delivery and earn ₹25 cashback, perfect for non-urgent orders.
Discounted Delivery Charges: Morning Delivery available at reduced rates per item.
No Minimum Order: Free Standard Delivery without a minimum purchase requirement.

Shopping and Savings

Prime membership is synonymous with exclusive savings and shopping benefits:

Amazon Pay ICICI Bank Credit Card: 5% back on Amazon.in purchases, plus additional rewards.
Prime Early Access & Exclusive Deals: Get first dibs on Lightning Deals and exclusive offers.
Prime Advantage: Enjoy No Cost EMI on smartphones and 6 months of Free Screen Replacement by Acko.

Digital Content

Dive into a world of endless entertainment with Prime's digital content offerings:

Prime Reading: Access a vast catalog of eBooks, comics, and more.
Amazon Music: Stream millions of songs and albums ad-free.
Amazon Prime Video: Unlimited streaming of movies, original series, live sports, and TV shows.
Prime Gaming: Free games every month and more gaming benefits.

Exclusive Member Benefits

Prime Lite Membership: A lighter version offering similar benefits but with some limitations, such as Morning Delivery at a higher rate and access to Prime Video on one device in HD quality with ads.

Navigating the Competitive Landscape in India

In the vibrant Indian market, Amazon Prime faces stiff competition across its multiple service categories:

E-commerce and Membership Programs

Flipkart Plus: Offers free delivery and early access to sales, with benefits earned through SuperCoins.
Snapdeal Gold: Provides free shipping, extended returns, and exclusive offers for digital payment users.

Video Streaming Services

Hotstar (Disney+ Hotstar), ZEE5, SonyLIV, Voot, and Netflix offer a diverse range of content, from live sports to original series, catering to varied viewer preferences.
MX Player: A free platform with a wide selection of TV shows, movies, and originals.

Music Streaming Services

Competitors like Gaana, Wynk Music, JioSaavn, and Spotify feature extensive music libraries and podcasts, appealing to music enthusiasts with their curated playlists and exclusive content.

A Diverse Competitive Landscape

The Indian market thrives on diversity, providing consumers with a plethora of choices tailored to their preferences. Amazon Prime, with its comprehensive bundle of services, stands out by offering an all-encompassing subscription that addresses a wide range of needs, from shopping and savings to unmatched digital entertainment.

This landscape ensures that Indian consumers can enjoy a variety of options, pushing service providers, including Amazon, to continuously innovate and enhance their offerings to meet and exceed customer expectations.

Service Benefits Matrix

Benefit/Service	Amazon Prime	Flipkart Plus	Disney+ Hotstar	ZEE5	Gaana	JioSaavn	Netflix
E-commerce Benefits	✓	✓
- Free Delivery	✓	✓
- Exclusive Deals	✓	✓
- Early Access to Sales	✓	✓
Video Streaming	✓		✓	✓			✓
- Original Series	✓		✓	✓			✓
- Movies & TV Shows	✓		✓	✓			✓
- Live Sports			✓
Music Streaming	✓				✓	✓
- Large Music Library	✓				✓	✓
- Ad-Free Listening	✓				Premium	Premium

In the competitive world of online subscriptions, various platforms offer distinct benefits and pricing models to attract and retain users. This matrix provides a comprehensive overview of the key features and costs associated with some of the leading services available in India.

Pricing Matrix

Service Provider	Subscription Tier	Cost (INR)
Amazon Prime	Annual	₹1,499
	Monthly	₹179
Flipkart Plus	Membership	Free (Earn SuperCoins)
Disney+ Hotstar	Mobile	₹499/year
	Super	₹899/year
	Premium	₹1,499/year
ZEE5	Monthly	₹299
	Annual	₹999
Gaana	Plus Monthly	₹99
	Plus Annual	₹399
JioSaavn	Pro Monthly	₹99
	Pro Annual	₹399
Netflix	Mobile	₹199/month
	Basic	₹499/month
	Standard	₹649/month
	Premium	₹799/month

Key Statistics for Amazon Prime in India

Over 60 million active users of Amazon Prime Video.
65% of Prime members are male, and 60% are under the age of 35.
Approximately 55% have a college degree.
Subscription services generated nearly $32 billion in revenue in 2021.

Global Impact of Amazon Prime

Over 200 million Prime members worldwide.
Sales during Prime Day reached $11.79 billion in 2021.
Accessible in 22 countries.
Retail subscription fees contribute $25.21 billion to Amazon’s annual revenue.

Pricing Tiers Recommendation

Basic Prime Tier

Pricing: ₹999/year or ₹99/month
Benefits:
- Free Two-Day Delivery on eligible items
- Access to Prime Video with ads (limited content)
- Prime Music with ads
- Limited access to Prime Reading and Prime Gaming
Rationale: Targets price-sensitive customers, offering a lower entry point with basic Prime benefits.

Standard Prime Tier

Pricing: ₹1,499/year or ₹149/month
Benefits:
- All Basic Tier benefits without ads
- Free One-Day Delivery on eligible items
- Expanded Prime Video content
- Exclusive deals and early access to sales
Rationale: Aligns with current Prime offering, balancing cost and value for the average consumer.

Prime Plus Tier

Pricing: ₹2,499/year or ₹249/month
Benefits:
- All Standard Tier benefits
- Free Same-Day Delivery in select cities
- Access to premium content on Prime Video, including international titles
- Family sharing for up to 4 family members
- Higher cashback on Amazon Pay
Rationale: Targets premium segment willing to pay more for enhanced services and exclusive content.

Pricing Approaches and Process

Cost Plus Pricing: Ensure each tier covers the cost of services provided, including delivery logistics and licensing fees for content, plus a fixed margin.
Competitive Pricing: The Basic and Standard tiers should be competitively priced against rivals like Disney+ Hotstar and Netflix, ensuring Amazon remains an attractive option for cost-conscious consumers.
Value Based Pricing: The Prime Plus tier is priced based on the perceived value of comprehensive benefits, exclusive content, and convenience, targeting customers willing to pay more for a premium experience.

Implementation and Experimentation

Bundling Options: Offer bundled services with Amazon devices (like Kindle or Echo) and partnerships with ISPs or mobile operators to increase value perception.
Discounted Rates: Provide introductory offers, student discounts, and annual payment discounts to encourage long-term commitments.
Experimentation: Pilot the new tiers in select markets to gather data on customer preferences and willingness to pay, adjusting strategies based on feedback and performance.
Continuous Learning: Monitor customer satisfaction, renewal rates, and competitive landscape adjustments, refining the pricing and benefits structure as necessary.

A/B Testing Proposals for the Indian Market

1. Video Streaming Content Preferences

Hypothesis: Adding more regional language films and series will increase subscription rates and viewer engagement among non-Hindi speaking demographics.
Test: Introduce a new section of regional content in two different formats: one as a separate category on the homepage and another integrated within existing categories but with a regional filter option.
Example: Compare engagement rates (subscriptions, watch time) between users exposed to separate regional categories versus those with integrated regional filters.

2. E-commerce Delivery Options

Hypothesis: Offering a late-night delivery option for prime products will increase conversions and customer satisfaction in metro cities.
Test: Implement a late-night delivery option (post 9 PM) for a select range of products in two major cities, comparing against the standard delivery options in control groups.
Example: Measure the increase in Prime subscriptions and repeat purchases in test cities versus control groups with standard delivery times.

3. Prime Membership Tiers

Hypothesis: Introducing a lower-priced, mobile-only Prime Video tier will attract younger demographics and increase overall Prime memberships.
Test: Offer a mobile-only Prime Video subscription at a reduced rate to a segment of users, and compare the subscription uptake against the standard Prime membership offering.
Example: Analyze the number of subscriptions, average watch time, and retention rates between the mobile-only tier and the full Prime membership.

4. Exclusive Deals and Offers

Hypothesis: Exclusive access to flash sales on electronics and high-value items will increase Prime membership renewals and upgrades.
Test: Provide early or exclusive access to flash sales on popular electronics to Prime members, and a regular sale for non-Prime users, measuring the impact on Prime sign-ups and renewals.
Example: Compare the Prime membership renewal rate and customer lifetime value before and after implementing exclusive flash sales.

5. Customized User Experience

Hypothesis: Personalized homepage recommendations based on past purchases and browsing history will increase product discovery and purchases among Prime members.
Test: Create two versions of the Prime homepage: one with standard, popular product listings and another with personalized recommendations.
Example: Evaluate the change in purchase frequency and basket size between users exposed to personalized homepages versus the control group.

6. Sports and Live Events

Hypothesis: Expanding live sports content beyond cricket to include local sports like Kabaddi and Football will broaden the user base and engagement.
Test: Stream select live Kabaddi and Football matches available exclusively to Prime members, and assess viewership and engagement metrics compared to IPL viewership.
Example: Measure new Prime sign-ups, average watch time, and retention rates during the sports season for both cricket and new sports offerings.

7. Local Delivery Services for Last-Mile Delivery in Rural Areas

Hypothesis: Partnering with local delivery services for last-mile delivery in rural areas will improve Prime accessibility and customer satisfaction in underserved regions.
Test: Collaborate with local delivery partners to offer Prime delivery services in rural areas, measuring the impact on delivery speed, customer feedback, and subscription.
Example: Compare delivery times and customer reviews between rural areas with and without local delivery partnerships, assessing the effectiveness of the initiative in expanding Prime's reach.

By conducting these strategic A/B tests, Amazon Prime can identify opportunities within the Indian market to drive growth and enhance its competitive edge.

In the dynamic landscape of digital subscriptions, Amazon Prime stands as a testament to innovation, customer-centricity, and strategic foresight, promising an enriched experience for its members across India.

Speed-Dataing for hackers!

Vivek0712 — Sat, 06 Jan 2024 17:40:30 +0000

For the ML enthusiasts, self-taught developers engaging in hackathons, and the professionals and data scientists striving to streamline ML processes, this blog is your compass in the world of machine learning. Whether you're tuning your ML operations or seeking a Rapid Application Development (RAD) strategy for Data Science, you've come to the right place.

You, my friend, are a hacker at heart if you resonate with any of the roles mentioned. As a seasoned hackathon expert engaged in 60+ hackathons and a professional MLOps Architect and startup consultant for AI/ML, I will demonstrate how to accelerate the machine learning journey from start to finish, all while maintaining the integrity and accuracy of your data and models.

Tackling Stroke Prediction with ML: A No-Code Journey with Cleanlab, Azure, and Amazon SageMaker

Unraveling the Stroke Prediction Dataset from Kaggle

In this blog, we embark on an insightful journey through the realm of machine learning, utilizing the Stroke Prediction Dataset from Kaggle as our guiding star. This dataset holds the potential to unravel predictive insights about stroke occurrences, based on parameters such as gender, age, health conditions, and smoking status. Each data entry is a window into a patient's profile, aiding us in understanding the likelihood of a stroke. Our goal? To predict whether a patient is at risk of a stroke (1) or not (0), using this rich dataset.

The No-Code Edge: Introducing Cleanlab

But here's the twist – we are going to do all of this without writing a single line of code! I introduce you to one of my secret weapons: Cleanlab. This tool is designed to simplify the entire machine learning process, from data cleaning to model selection, making it accessible even to those without extensive coding experience. To validate our approach, we will be benchmarking our results specifically against one of the prominent cloud-based AutoML tools: Azure Automated Machine Learning.

Exploring Cleanlab Studio: Uploading Your Dataset

Getting Started with Cleanlab Studio

To begin your no-code machine learning journey with Cleanlab Studio, follow these steps:

Visit Cleanlab.ai and sign up for an account or visit their GitHub account.
Utilize the free trial offer to get started.

Navigating the Dashboard

Once logged in, you'll be greeted with an intuitive dashboard. Here's what you'll find:

Projects Section

List of ML Projects: Each project is associated with a dataset and includes details such as dataset quality and issues resolved.

Datasets Section

Dataset Listings: Find datasets by name along with details like modality, the number of rows, and upload date.

Uploading Your Dataset

Upload Dataset: Click "Upload Dataset" on your dashboard.
Select File: Choose "Upload from your computer" and select the stroke prediction dataset file.
Dataset Details: Confirm dataset name and designate an "ID column" for unique data identification.

Schema Confirmation: Review the detected fields (columns) by Cleanlab Studio.

Dataset Preview: Visually inspect a sample of your dataset to ensure correctness.

## Creating a Project and Cleaning Data in Cleanlab Studio

After uploading your dataset, proceed with the following steps to create a project and clean your data:

1. Create Project

Click on the + Create Project button once your dataset is ready.

2. Project Details

Enter a name for your new project, such as "Stroke Prediction". Select the appropriate cleaning task for your data, which in this case is "Tabular Classification".

3. Type of Classification

Choose "Multi-Class" as the type of classification since each data point in the stroke prediction dataset corresponds to one outcome.

4. Label Column

Identify the "stroke" column as the label for prediction within your dataset.

5. Dataset Sample

A sample of your dataset is provided for review. Ensure that the data appears as expected.

6. Feature Fields

Check the feature fields you wish to include in your machine learning model.

7. Use Cleanlab Auto-ML

Enable Cleanlab Auto-ML to allow the platform to automatically train and combine multiple models for optimal data cleaning.

8. Project Setting

Select between the "Fast" and "Regular" cleaning options. "Fast" provides quicker but potentially less precise results, while "Regular" is more thorough and yields higher quality at the cost of more time.

9. Start Cleaning

Click Clean my data to initiate the auto-cleaning process. Cleanlab will process your data and send an email notification when the results are ready.

Insights from Data Cleaning and Model Evaluation

After the data cleaning process, Cleanlab Studio has meticulously identified specific issues within our dataset.

The platform's auto-training feature has evaluated several machine learning models to determine the best performer with the cleaned data.

Improving Data Quality with Cleanlab Studio

Clean Top K

This feature offers a bulk action capability, allowing you to address issues across numerous data points at once. The actions you select here will affect the top data points, which are determined by the currently applied sort and filter settings within Cleanlab Studio.

Auto-fix

When you choose the 'Auto-fix' option, Cleanlab Studio automatically applies its recommended actions to the selected data points.

Steps for Training and Deployment

Name your model, e.g., stroke-prediction-cleaned-model.

Click Deploy Model to begin the process.

Post-deployment

Track the deployment status on the dashboard.
Review the model evaluation for accuracy and performance metrics.
Use the provided Python API for integration, with your API key for secure access.

Deploying your model is now just a click away, paving the way for insightful predictions.

Benchmarking Against Azure Automated Machine Learning

Typically, following the established steps for creating a machine learning model with Azure Automated Machine Learning, we now compare the results.

Azure Model Training Overview

Algorithm: A Voting Ensemble approach was utilized.
AUC Weighted: Achieved an AUC weighted score of 0.85427.
Accuracy: The model attained an accuracy of 95.127%.

Cleanlab's AutoML Advantages

When juxtaposing Cleanlab's AutoML results with those from Azure's Automated Machine Learning, it's evident that Cleanlab stands out, particularly in terms of accuracy. Cleanlab not only matches but often surpasses the precision of Azure's offerings in term of straightforwardness of cleaning and training data, lesser training time

Streamlined Model Deployment and Evaluation

Cleanlab's platform is designed for simplicity and ease of use, streamlining the deployment process. It ensures that models are not only deployed efficiently but also monitored with a user-friendly interface, offering a clear and concise view of your model's performance.

Conclusion

The Stroke Prediction Dataset from Kaggle served not just as a dataset but as a gateway to understanding a critical global health issue. With the World Health Organization citing stroke as the 2nd leading cause of death, our problem statement transcended a mere technical challenge; it became a mission to potentially save lives through predictive analysis.

The Cleanlab Studio, with its no-code AutoML solution, demonstrated that sophisticated machine learning is not confined to experts. It showed that with the right tools, we could make significant strides in medical predictions, even under the crunch of hackathon and organisation deadlines. Our comparative analysis with Azure's Automated Machine Learning further highlighted Cleanlab's efficiency and accuracy, bolstering our confidence in the solutions we developed.

As we draw this blog to a close, the key takeaway is clear: the union of purposeful problem statements with powerful, accessible ML tools like Cleanlab can lead to breakthroughs in not just technology but also in societal well-being. This project was more than an exercise in ML proficiency; it was a step towards leveraging technology for the greater good. The path ahead is promising, and as we continue to innovate, we do so with the hope of making a tangible impact on the world.

Forem: Vivek0712

Introducing AgentRegistry: Centralized, Flexible Agent Management for Strands Agents SDK

Introduction

Why Do We Need an Agent Registry?

The Problem

The Solution

What is AgentRegistry?

Feature-by-Feature Breakdown

1. Centralized Registration and Discovery

2. Optional Session-Scoped Management

3. Tool Subset Mapping for Specialization

4. Clean, Lightweight, and Optional Integration

5. Backward Compatible and Developer Friendly

Intrinsic Details: Naming, Auto-Generation, and Retrieval

How Are Agent Names Handled?

Full Example: Multi-Agent, Session-Scoped Workflow

When Should You Use AgentRegistry?

Developer Insights

Comparison Table

Conclusion

🕵️‍♂️ Building a Cybersecurity CTF Game with Amazon Q Developer CLI

🕵️‍♂️ Building a Cybersecurity CTF Game with Amazon Q Developer CLI

🌟 Why I Built a Terminal-Based Digital Forensics Game

🤖 How Amazon Q Developer CLI Helped Me Build This Game

✅ My Prompting Strategy

💡 Prompting Techniques That Worked

🧠 How Amazon Q Developer CLI Solved Key Challenges

🧵 1. Managing Game State

🎨 2. Real-Time Visual Corruption

🧰 3. Realistic Command Simulation

⏰ Game Systems & Mechanics

🗂️ Filesystem & Terminal UI

🔍 Memory Dump Example

🧠 Adaptive Hint System

🛠️ System Corruption

🧭 Tab Completion & Command History

⚙️ Time-Saving Features Built by Amazon Q Developer CLI

🎯 What Players Learn

Technical Skills

Soft Skills

📊 Dev Metrics

🧪 Future Features

🎉 Final Thoughts

From a Curious Student to Meeting Satya Nadella: My Journey with the Azure Developer Community

🚀 The First Leap of Faith

🌱 Community: The Catalyst for Growth

🤝 Meeting the Visionary: Satya Nadella

❤️ Giving Back to the Community

🗣️ Talks and Sessions: A Chronicle of Knowledge Sharing

🎥 Video Sessions:

📝 Blog Posts on Dev.to

🎓 Certifications: The Foundation of My Technical Knowledge

Microsoft Azure:

🌐 VickyBytes: A New Chapter

👨‍💼 My Professional Journey: A Snapshot

🌟 Lessons from the Journey

🙏 A Heartfelt Thank You

Photo Gallery (Random moments clicked!)

Attendance System with Face Mask Detection using Azure Face API

Prerequisites

Architecture at a Glance

Code Snippet #1: Door Access Control With Face Recognition

How It Works

Real-Time Webcam Detection

How It Works

Integrating with an Attendance Database

Troubleshooting & Best Practices

Next Steps:

Predict NYC Taxi Fares | End-to-end Azure MLOps with GitHub Actions

Table of Contents

Introduction to AI Workloads and Operational Methodologies

Understanding GitHub Actions

Why Azure Machine Learning with GitHub Actions?

Prerequisites

Set Up Authentication with Azure and GitHub Actions

Create a Service Principal

Assign Permissions to the Service Principal

Set Up the GitHub Repo

Deploy the Machine Learning Project Infrastructure with GitHub Actions

Next Steps

6.2 Example Inference Script (`score.py`)

4. Deploy a Model (Pick Your Brain: `gpt-4o`, `gpt-4`, etc.)