Forem: Fahad Abid

AI Repos Hub

Fahad Abid — Mon, 01 Sep 2025 20:04:14 +0000

Curious about AI but don’t know where to start?

Here’s a curated collection of AI learning resources — all in one place, neatly organized, and easy to explore.

📚 What you’ll find inside:

🔗 Direct links to some of the best AI-focused GitHub repositories

👩‍💻 Info about the creators behind them

⭐ Popularity insights (so you know what the community loves)

📊 Regularly updated lists, so the content always stays fresh

Whether you’re a student, beginner, or developer, this acts like a living AI library — giving you quick access to resources that can help you learn, experiment, and grow.

✨ Explore the repo here: https://github.com/fahadabid545/ai-learning-repos

AI #Learning #OpenSource #GitHub #MachineLearning

🧪 Managing Machine Learning Experiments with MLflow and Weights & Biases (W&B)

Fahad Abid — Sun, 01 Jun 2025 20:45:31 +0000

Tracking machine learning experiments isn’t a luxury—it’s essential. As a Junior AI Engineer working on multiple models and pipelines, I’ve learned how critical experiment tracking becomes once your project moves beyond a Jupyter notebook.

In this post, I’ll walk you through:

🔄 Why experiment tracking matters
🧰 The difference between MLflow and W&B
⚙️ How I use them in real projects
✅ When to use which tool

🚨 The Problem

You trained a model last week. It worked. But now…

What features did you use?
What hyperparameters gave the best accuracy?
Where’s the version of the dataset you used?

Without tracking, you're relying on memory (bad idea) or scattered notes (worse idea).

🛠️ MLflow vs. Weights & Biases

Feature	MLflow	Weights & Biases (W&B)
Setup	Simple, local-first	SaaS + Local support
UI	Minimal, self-hosted	Rich, interactive dashboard
Logging	Metrics, params, artifacts	Metrics, params, images, more
Integration	Great with Python + REST API	Strong for deep learning
Hosting	Self or Databricks	Free cloud tier available
Use Case	Classical ML, corporate use	Deep learning, team projects

⚙️ My Setup (Real-World Use)

MLflow

I use it for:

Sklearn pipelines
Traditional ML models (XGBoost, Random Forest)
Tracking metrics & saving artifacts
Auto-logging with mlflow.sklearn

import mlflow
import mlflow.sklearn
from sklearn.ensemble import RandomForestClassifier
from sklearn.metrics import accuracy_score

with mlflow.start_run():
    model = RandomForestClassifier()
    model.fit(X_train, y_train)
    mlflow.sklearn.log_model(model, "model")
    mlflow.log_metric("accuracy", accuracy_score(y_test, model.predict(X_test)))

## 🧪 Weights & Biases

**Used for:**

- Deep learning (Keras / PyTorch)
- Logging training curves, images, system metrics
- Comparing dozens of runs interactively

python
import wandb
from wandb.keras import WandbCallback

wandb.init(project="cnn-project")

model.fit(X_train, y_train, epochs=10, callbacks=[WandbCallback()])

🧠 Lessons Learned
Log everything early. You’ll thank yourself later.

Pick the right tool for the job: MLflow for structured ML, W&B for dynamic DL.

Use tags and versioning so your team (or future self) can make sense of experiments.

📌 Final Thoughts
Experiment tracking is like version control for your brain.
If you're working on even slightly complex projects, start logging today—before you're 20 experiments deep in chaos.

Have you used MLflow or W&B?
Or do you rely on spreadsheets and screenshots (no judgment 😅)?
I'd love to hear your workflow in the comments below!

🚀 Check Out My AI Portfolio — Projects, Resume, and More!

Fahad Abid — Sun, 01 Jun 2025 20:45:31 +0000

Hey Dev Community! 👋

I’m Muhammad Fahad, currently working as a Junior AI Engineer, and I’ve just launched my AI Portfolio Website. It’s a central place where you can explore my projects, tech stack, and grab my resume.

🔗 Explore My Portfolio
🖥 Live Portfolio Site: https://fahadabid545.github.io/Portfolio/

This portfolio includes:

✅ AI/ML projects with GitHub links

📊 Data visualizations using Tableau

🧠 NLP, Computer Vision, and MLOps work

📄 My resume

🛠 Tech Stack

Languages:
Python, R, SQL, NoSQL

Libraries & Frameworks:
Scikit-learn, TensorFlow, Keras, PyTorch, Spacy, NLTK

Data Engineering:
Pandas, NumPy, Snowflake, MySQL, MongoDB

Visualization & Analysis:
Matplotlib, Seaborn, Plotly, Jupyter Notebook, Excel, Tableau, Power BI, EDA, ETL

API Development:
FastAPI, Flask, RESTful API, Postman

Cloud & MLOps:
AWS, GCP, MLflow, W&B, ETL Pipelines

Version Control & Tools:
Git, GitHub, Jira, Notion

🚀 Exploring the Future: Where AI Meets Web3

Fahad Abid — Mon, 26 May 2025 11:30:00 +0000

Hey Devs! 👋

I'm Fahad, a Data Science enthusiast diving deep into the intersection of Artificial Intelligence/Machine Learning and Web3 technologies. 🔗🤖

I believe the future lies in decentralized intelligence—combining the predictive power of AI with the transparency and security.

🧠 Currently exploring:

Building and optimizing ML models for real-world decision-making
Exploring Federated Learning for privacy-aware AI solutions
Experimenting with decentralized approaches to data sharing and computation

I'm here to learn, share insights, and collaborate with like-minded builders. Let’s shape the future of intelligent, secure, and open technologies together! 🌐✨

Follow me if you're into AI, ML, Blockchain, or Web3—content, ideas, and experiments coming soon!