Forem: Ranga Bashyam G

Architecture, Deployment & Observability - The Part Nobody Warns You About

Ranga Bashyam G — Tue, 03 Mar 2026 11:55:00 +0000

Have you ever wondered where the actual problem starts in a software lifecycle?

Most people say requirement gathering. Understanding the goals, the vision, the stakeholder expectations... yes, those matter, absolutely. But honestly? That's not where things fall apart.

The actual mess starts at technical planning. Architecting the solution, then trying to execute that architecture on real infrastructure that never behaves the way your diagram assumed, that's where the cracks appear first. And once the foundation has cracks, no amount of clean code or good intentions covers it up.

Architecture Is a Negotiation, Not a Blueprint

Here's what I've learned after architecting multiple tools and services, Architecture is never purely a technical exercise. It's always a conversation between what the business expects, what the infrastructure can actually handle, and what your team can realistically build and maintain without burning out in 3 months.

Every decision you make cascades. You pick Apache Airflow today, two years later your team is debugging consumer lag at 2 AM. You go DAGs for speed, now scaling is a 6-month refactoring nightmare. You choose a managed cloud service to save time, now you're locked into their pricing decisions forever.

There is no perfect architecture. The job is picking the right trade-off for the right context and being honest about what you're giving up.

The trade-offs nobody wants to have (but you have to):

Consistency vs availability - pick a side, document why
Stateless vs stateful - each has infra implications your ops team will live with long after you move on
Managed cloud services vs self-hosted - a cost vs control conversation that needs actual numbers, not vibes
Microservices vs monolith vs modular monolith - "it depends" is fine, but it depends on what needs an answer

The engineers who become architects aren't the ones who know every pattern. They're the ones who know which pattern to not use in a given situation. That's the real experience.

Making Your Client Understand ! This Is the Hardest Part !!

Okay so here's the thing nobody prepares you for.

You've done the analysis. You know exactly what the limitations are. You know why the proposed approach won't scale the way the client thinks it will. You know the infra constraints are real. Now you have to explain that to someone who paid good money and has expectations you can't fully meet with the given resources.

If you keep explaining the scarce resources in technical terms, the bid is off. They value you low. Not because you're wrong, but because you failed to translate the constraint into something they actually understand.

Lead with outcomes. "This approach handles a 10x traffic spike without manual intervention" lands better than "we're implementing HPA with custom metrics on Kubernetes." Both mean the same thing. Only one keeps the client in the room.

The real game is making your client understand the boundaries while also showing them the best of what's possible within those boundaries. That takes experience. Technical knowledge. And honestly, a lot of patience too.

Deployment, Where Architecture Meets Real Life

Architecture on paper and architecture in production are two very different things.

I've seen beautiful designs fall apart the first time they hit real network latency, real disk I/O, and real users doing things nobody anticipated in the design session.

Kubernetes is the de-facto orchestration layer now. It's powerful. It's also unforgiving. If you don't understand what you're deploying into... node affinity, resource requests vs limits, pod disruption budgets, k8s will punish you with fancy errors and silent failures at the worst possible time.

Single Cloud vs Multi-Cloud - Companies Are Splitting on This

Nowadays companies are moving in two directions. One is a specific cloud-oriented approach, the other is multi-cloud. Both are valid. Both have real costs that go beyond the invoice.

Single cloud gives you depth, tighter integrations, simpler operational model, better managed service compatibility. You're betting on one vendor's roadmap and pricing history though.

Multi-cloud gives you resilience and leverage... no single provider outage takes you down, no pricing lock-in. The cost is complexity. Your infra team is now managing abstractions across two or three different API paradigms, IAM models, and networking topologies at the same time.

Only a clever and well-budgeted team survives cloud optimally. The cloud is pay-as-you-go ! but waste is also pay-as-you-go. And waste compounds faster than most teams realize.

AI Workloads Changed the Deployment Problem

People used to worry about hosting AI models locally,considering the workload, stability, scalability, performance, latency, accessability and mainly on privacy. So everyone moved to cloud. Now the problem is: cloud is pay-as-you-go and the charges are heavier than expected.

GPU-backed compute is expensive. Cold start latency on inference endpoints is different from a stateless REST API. Token-by-token generation means time-to-first-token and total generation time are completely different signals with completely different infra implications.

The API call looks cheap. The infrastructure required to make that API call reliable, fast, and cost-efficient at scale, that's where the real engineering work is hiding.

Observability! You Don't Know What You Don't Measure

Here's the uncomfortable truth. You don't actually know what your system is doing in production. You know what you think it's doing. You know what it did in staging. You know what it looked like in the load test.

Production is a different animal.

Observability is not monitoring. Monitoring tells you something is wrong, it's the alarm. Observability is the ability to ask arbitrary questions about your system's internal state based on the signals it produces. It's how you go from "something is broken" to "here is exactly why, and here is exactly where."

Metrics, Logs, Traces... All Three, No Skipping

Metrics - what is happening right now. Your SLA dashboard, your capacity planning input, your early warning system
Logs - what happened. Invaluable for debugging, but expensive and noisy at scale if you're not intentional about log levels and sampling
Traces - how it happened. The full request journey across distributed services. In a microservices world, traces are non-negotiable. Without them, debugging a latency spike across four services and two external APIs is just educated guesswork

The mistake most teams make is treating observability as something you bolt on after the system is built. By then the instrumentation is an afterthought, naming conventions are all over the place, and you're collecting the signals that were easy to add, not the ones that are actually useful.

Observability for AI Systems Is a Different Problem

Traditional APM tooling wasn't built for LLM workloads. Latency behaves differently. But beyond infrastructure metrics, AI systems need semantic observability.

Did the model return something useful? Was the retrieved context in the RAG pipeline actually relevant? Is the prompt structure degrading as edge cases accumulate over time?

CPU utilization and memory graphs can't answer those questions. You need eval pipelines, response quality sampling, feedback loops embedded into the product itself. That's a different layer of observability and most teams aren't thinking about it yet.

Your Cloud Bill Is a Signal Too

In cloud-native environments, an unexpected cost spike is often the first indicator of a misconfiguration or a runaway process. Engineers who treat FinOps as someone else's problem eventually end up in a very awkward conversation with leadership trying to explain why infra costs tripled.

Tagging resources, attributing costs to services and teams, anomaly alerts on spend, this is observability work. It belongs in the same operational posture as your Prometheus dashboards.

Where It All Ties Together

The cloud is more capable than ever. AI capabilities are an API call away. Kubernetes lets you orchestrate globally. The tooling exists and it's genuinely impressive.

But tooling is not a substitute for craft.

The engineer who can design a system that survives its own success, deploy it reproducibly and observably, and instrument it to give genuine insight into its actual behavior that engineer is rare. And that combination is what actually moves organizations forward.

The gap between a system that technically works and a system that is production-ready, cost-efficient, observable, and maintainable, that gap is not small. In most projects, that gap is the majority of the actual engineering effort.

Requirement gathering gave you a direction. Architecture, deployment, and observability are the journey.

Anyone can deploy a service. Fewer can architect one that lasts. Fewer still can tell you, at any moment, exactly how that service is behaving and why.

That's the skill set. That's the discipline. And as AI workloads and cloud-native systems keep evolving, the engineers who invest in all three, not just the one they find most interesting are the ones building the infrastructure the next decade runs on.

The cloud isn't magic. Kubernetes isn't magic. AI isn't magic. The craft is understanding deeply enough to make it look that way.
~ Ranga Bashyam

Observability- My New Experience and Beyond

Ranga Bashyam G — Tue, 25 Nov 2025 14:50:45 +0000

From AI/ML Background...

In this article, I’m trying to jot down my journey, moving from being an AI engineer, living deep in models, data, and drift, to stepping into the world of observability. I’m not going too deep into the transition itself, but more into what I’ve learned about observability: its purpose, where it fits, how to use it, and the stack that actually makes sense in real-world engineering.

If you come from AI or ML, you probably think you get monitoring. We keep an eye on pipelines, stare at dashboards, track every metric we can get our hands on. We’re obsessed with recall, precision, AUC, all those numbers that tell us if the model’s still alive. In MLOps, it’s all about performance. Is the model still making sense in the real world? Should we retrain? When do we push the next checkpoint? And, most importantly, how do we do it without breaking everything for users? That’s the game: ship the next version, quietly, while everyone keeps moving along like nothing happened.

But in modern cloud systems, what we track and how we track it is a completely different beast.

Today we have scrapers, agents, fetchers, exporters, service meshes, sidecars, dashboards, all trying to answer one single question: “What’s happening?”
The irony? Teams pour time and money into these tools and still don’t have a real handle on their systems. I’ve seen organizations with the best monitoring stack, tons of fancy dashboards, and still nobody knows what’s actually going wrong when something breaks. It’s frustrating because most of these setups tell you things are happening, not why they are happening.

That’s where the transition hit me hard.

ML monitoring is narrow, purpose-driven.
Cloud observability is wide, chaotic, systemic.

In AI/ML, the model gets all the attention. It’s the prize everyone’s guarding, and most of our work goes into making sure it stays useful. We keep an eye on data pipelines so nothing goes stale, check if our predictions still match what we saw during training or local env workings, like its not only a ML model, even If we deploy a RAG model or even you use any LLMs, we just see the actul output and the Cost trackings only.

But observability?
It’s not about one component. It’s about everything, every request, every microservice, every hop, every node, every storage layer, every unexpected side effect in your system.

That shift changed how I saw things. I stopped being the person obsessed with just the model and started seeing the whole system as this messy, living thing. Once you dive into observability, you stop asking, “Is it up?” You start asking, “When it goes down, how will I know why?”

And that’s the foundation of this article.

Observability Isn’t Just Dashboards, It’s How You Keep Your Head Above Water in Production

If you’ve spent any time wrangling production systems, you know the drill. The dashboards look perfect, everything’s good, CPU and memory numbers are steady, and the services say they’re “healthy.” Then out of nowhere, users start yelling, latency spikes, and suddenly the business is losing money. That’s when you get it: observability isn’t just another layer on top of monitoring. It’s what keeps you from drowning when things go sideways.

Introducing Signals: Golden Signals, LEST, and the Power of Percentiles

Let’s be real! we engineers get attached to averages, but users? They notice the outliers, the rough edges, the long wait times, the weird glitches. Say your P99 latency suddenly jumps from 110ms to four seconds. The average still looks fine, but users are losing their patience. That’s why you need to nail the Golden Signals: Latency, Errors, Traffic, Saturation. They might sound boring, but they’re the backbone of almost every incident. Track latency spikes with traces, hunt down errors in logs using correlation IDs, figure out if traffic bursts are real people, retries, or just bots, and don’t just trust pretty dashboards, check your queues and throttles.

Then there’s LEST: Logs, Events, Spans, Traces. This is where engineers really get their hands dirty. Logs tell the story, great for debugging and post-mortems. Events flag the big moments. Spans break down what’s happening inside those complex, distributed requests. Traces show you the whole system in motion. Think of metrics as the rough sketch, logs as the details, traces as the journey, and events as the why behind it all. When you pull these threads together, troubleshooting stops feeling like digging through rubble and starts feeling like solving a mystery with all the right clues.

The Pillars of Service: Reliability Goes Beyond Metrics

Here’s something you pick up fast in the trenches, a lot of teams get obsessed with flashy dashboards and think that’s all observability is. The real pros? They see observability as the backbone of reliability engineering.

Let’s break it down. First, there’s Availability. SLAs, SLOs, and SLIs get thrown around a lot, but they’re not just corporate jargon. They’re what help you actually manage pain, your pain, the users’ pain, everyone’s pain. If your on-call folks wake up every other night, your metrics are lying to you. SLOs force you to pay attention to what users really feel, not just what looks pretty on a screen.

Then you’ve got Performance. Everyone loves a good average, right? But the real problems where users start cursing your name, hide in those nasty outliers: P95, P99 latencies, all that. That’s the stuff that makes or breaks user experience.

Last up, Reliability. Reliable systems aren’t the ones that never break. They’re the ones that break in obvious, contained ways, and recover fast. That’s what strong engineering looks like when it’s actually running in production.

Building the Observability Stack

When you get telemetry right, you stop guessing and actually start solving problems. This isn’t about collecting every tool out there, it’s about how they work together when you’re on-call and things are going sideways. Prometheus is your go-to for metrics, grabbing data from exporters all over the place. Just be careful with labels,if you use things like user IDs, UUIDs, or timestamps, Prometheus will slow to a crawl.

Grafana’s the dashboard you actually want to look at. If you’ve got 20 panels jammed in, it’s basically a screensaver, not something that helps you in a pinch. Stick to what matters: error rates, latency percentiles, traffic spikes, and how close your infrastructure is to maxing out. That’s what keeps you afloat.

Loki’s great for logs and won’t destroy your budget. Think of logs as structured stories with correlation IDs, you want to connect the dots, not drown in endless lines of noise (and definitely not rack up a monster cloud bill).

Once your setup grows, Mimir comes in handy with multi-tenancy, long-term storage, and distributed metrics. Suddenly, keeping data around isn’t just a financial headache, it’s a feature.

Tracing tools like Tempo or OpenTelemetry give you superpowers. When calls between services start dragging, a trace tells you exactly where things are stuck—like spotting Service B endlessly retrying because Redis is timing out at just 10% saturation. Finding details like that can save you hours when chaos hits.

Observability as a Culture

Observability isn’t all shiny dashboards and smooth graphs. It’s messy. Every engineer finds out the hard way. Outages? They almost never wave a flag, you have to dig. High cardinality? That’ll wreck your clusters long before you run out of CPU. Logging everything sounds smart, but honestly, it just creates a pile of noise. Alert fatigue wears you down fast. Skip correlation IDs and you can forget about real debugging. And if you’re not paying attention, vendors will eat your budget for breakfast. Even dashboards can get out of hand, sometimes they end up as vanity projects, not real tools. The worst? A gorgeous dashboard that goes silent when everything’s burning.

Here’s the truth: observability isn’t just about tech. It’s about how you work. If developers don’t instrument their code, Operations spends their days putting out fires. Good telemetry starts with devs, emit the right metrics, keep logs structured, use span contexts, skip random labels, and actually respect retention policies. Blame-free post-mortems matter. Alerts should make sense. SLOs should match what users actually care about. A solid system isn’t one that never breaks. It’s one that tells you, loud and clear, when it does.

Wrapping It Up

Observability isn't a checkbox or "slap on Grafana and done." It's a discipline that flips incidents into lessons, mess into method, and dashboards into honest mirrors. Every engineer learns this eventually, often painfully: healthy metrics don't guarantee a healthy system. Get that, and you shift from prettifying screens to crafting systems that talk back meaningfully.

Why Kubernetes is the Safety Net for Your AI Circus ?

Ranga Bashyam G — Tue, 26 Aug 2025 06:19:36 +0000

Why Kubernetes Matters for AI (Setting the Stage)

Let's be honest: I've worked in multiple deployments and AI workloads differ from standard web applications. Deploying a large language model, recommendation engine, or GPU-intensive computer vision pipeline is far harder than operating a React frontend or a small backend service. These devices use a lot of resources, including GPUs, TPUs, large memory pools, quick disc I/O, distributed clusters, and incredibly efficient auto-scaling. That's precisely what Kubernetes (K8s) is for. Fundamentally, Kubernetes functions as a traffic cop, power grid, and app repair system. It ensures that resources are allocated equitably, that containers do not collide, and that when something dies—which is inevitable in AI—it simply spins it back up. Put another way, Kubernetes makes implementing AI apps more about innovating than it is about putting out fires.

Kubernetes Core Concepts in Plain English

Let's boil down Kubernetes before getting into GPU nodes and AI pipelines. In the Kubernetes object model, pods are the smallest deployable unit. They are essentially atomic scheduling entities that contain one or more containers, usually a single primary container for your AI model inference server (such as a TensorFlow Serving instance) and optional sidecar containers for monitoring or logging. Whether virtual machines (VMs) or bare-metal servers, nodes are the underlying worker machines that make up the cluster's compute layer.These machines run the Kubelet agent to orchestrate pod lifecycle, along with other components like network proxies and the container runtime (such as containerd or CRI-O).

In order to ensure high availability for your AI workloads, ReplicaSets automatically scale or replace failed pods and declaratively maintain a stable set of replicated pods by continually evaluating the current state against a desired replica count.Using techniques like Recreate or RollingUpdate to reduce downtime during model retraining deployments, deployments expand on this by offering a high level abstraction for managing ReplicaSets and enabling rolling updates, rollbacks, and versioning. With types like ClusterIP for internal traffic, NodePort for external exposure, or LoadBalancer for cloud-integrated ingress—essential for routing requests to distributed AI endpoints—services serve as an abstraction layer for network access, defining a logical set of pods via label selectors and providing a persistent IP and DNS name, regardless of pod rescheduling or failures.

Additionally, Kubernetes offers ConfigMaps for injecting non-sensitive configuration data (such as database URLs or hyperparameters) as environment variables, volumes, or command-line arguments, and Secrets for handling sensitive data (such as model weights or API tokens for cloud storage) in a base64-encoded, encrypted-at-rest format to prevent exposure in etcd or pod specs—both of which are crucial for protecting AI models that are vulnerable to intellectual property risks or integrations with services like Hugging Face or AWS S3.

From a basic rule-based chatbot to a resource-intensive multimodal AI system that uses distributed training across heterogeneous hardware, Kubernetes can be seen as modular Lego blocks for orchestrating containerised applications at scale once the basics are understood.

Why AI Needs More Than "Vanilla Kubernetes"

The twist is that utilising Kubernetes to deploy a small Node.js API is similar to packing a backpack. AI deployment is akin to attempting to lift an elephant. We are discussing distributed training, GPU scheduling, massive data transfer, and extremely low latency requirements. GPUs are not automatically understood by vanilla Kubernetes. To even request GPU resources, you need specialised schedulers or NVIDIA device plugins. The same is true for storage: AI datasets reside in terabytes, frequently in distributed file systems or S3 buckets, rather than in tidy little SQLite files. If you carefully plan your cluster, resource requests, and auto-scaling rules, Kubernetes can manage this. AI is about "it runs reliably, even when it's absurdly heavy," not "it runs."

Kubernetes and GPUs – The Real Love Story

Now, let's look at how you can make Kubernetes "GPU-aware" for AI deployments. This is because, by default, Kubernetes treats GPUs like they were some strange, alien technology that it is unable to manage. It's excellent at controlling CPU and memory by default, but GPUs? It has no idea. This is where the NVIDIA Kubernetes Device Plugin is useful; it functions similarly to a translator for Kubernetes, allowing it to comprehend GPUs. After installing this plugin, your pods will be able to request GPUs in the same manner that they do for CPU or memory. Saying something like, "Hey, Kubernetes, this AI training job needs two GPUs," will ensure that the pod lands on a node that has those GPUs available. No speculating or scheduling on a poor CPU-only node.

Now, you can get fancy with GPU pools for things like running large AI models for inference (think LLaMA for text generation or Whisper for speech-to-text). You're essentially posting a "VIP only" sign on your GPU nodes when you set up taints and tolerations. "Don't schedule just any random pod here—this node is for GPU-heavy workloads only," Taints advises Kubernetes. Giving your AI pods a VIP pass to get around that restriction is what tolerances are. This prevents random microservices, such as a web server or logging agent, from clogging up your GPU nodes. It's similar to making sure your Ferrari isn't stuck transporting groceries and is instead saved for fast races. By keeping your AI workloads running smoothly, this configuration makes the most of those expensive GPUs for the demanding tasks for which they are designed.

Scaling AI Models with Kubernetes

Now, let's talk about scaling, as this is where Kubernetes really stands out. Imagine having a chatbot that is powered by a powerful AI model, such as a customized conversational beast or a refined LLaMA. Everything is going well until suddenly there is a spike in traffic due to your bot going viral on X. Do you want to be that person who is constantly SSHing into servers and manually starting containers to manage the load? No way, that would be a nightmare. Now, since this is where Kubernetes really shines, let's talk about scaling. Imagine a chatbot that is powered by a powerful AI model, such as a customized conversational beast or a refined LLaMA. Everything runs smoothly until suddenly your bot goes viral on X, causing traffic to spike. Do you really want to be the person who is constantly SSHing into servers and manually starting containers in order to manage the load? No, that would be a nightmare.

But sometimes, just adding more pods isn’t enough, especially for AI workloads that are super resource-hungry. That’s where the Vertical Pod Autoscaler (VPA) comes in. It’s like a personal trainer for your pods, tweaking their resource requests—bumping up memory or GPU allocation if your model’s inference needs more juice, or dialing it back to avoid wasting resources. It’s smart enough to figure out what your pods actually need to keep things running smoothly. And when your nodes are maxed out and even more pods aren't working? The Cluster Autoscaler is now available. This bad boy says, "Hey, give me more nodes," to your cloud provider, be it AWS, Google Cloud, Azure, or even IBM Cloud. In order to prevent your chatbot from crashing and burning under the viral spotlight, it spins up new machines to join your cluster. After the excitement subsides, it reduces everything to spare you from a huge cloud bill. The real magic? All this happens automatically, without you breaking a sweat. So when your AI demo blows up on X and the world’s hammering your endpoint, Kubernetes has your back, keeping things cool while you soak up the glory.

Worst-Case Scenarios: When AI Goes Wrong

Let’s be real — AI deployments break. Now, let's explore the messy reality of using Kubernetes to run AI workloads. If you're not careful, things can quickly go awry. Imagine a scenario where a single malicious job consumes all of the GPU memory while you are training a large model, causing the entire node to crash. Or perhaps there is a sneaky bug in your PyTorch code that is causing your pod to restart as if it were caught in a bad loop. Even worse, your fancy distributed training job is slower than your old laptop running a Jupyter notebook due to a network bottleneck. To keep your AI workloads running smoothly, you must properly configure Kubernetes, which offers you the tools to deal with these issues.

To get started, Kubernetes provides readiness and liveness probes to monitor pods. Similar to a heartbeat monitor, liveness probes allow Kubernetes to detect and automatically restart your pod in the event that the main process—such as your training script—died or froze. Crash containers will no longer be babysat. Conversely, readiness probes verify that your pod is truly prepared to manage traffic before forwarding requests to it. To keep users from hitting a half-baked pod, Kubernetes delays sending traffic to your inference server while it is still warming up or while the weights of your model are loading.

Resource limits are another issue, which is similar to putting a leash on avaricious containers. Kubernetes allows you to specify the precise amount of CPU, memory, or GPU that a pod may use. In order to prevent the node from crashing and to protect other workloads, Kubernetes intervenes if your training job tries to use up all of the GPU memory. But when things get really hairy—like when you’re juggling critical inference APIs alongside resource-hogging training jobs—you need to think bigger. This is where node pools, priority classes, and PodDisruptionBudgets (PDBs) come in. Node pools let you group nodes by their role, like having a dedicated pool of GPU-heavy nodes for training and another with lighter GPUs for inference. By using taints and tolerations (like we talked about before), you ensure training jobs don’t accidentally land on your inference nodes, keeping your API snappy.

Priority classes let you tell Kubernetes what’s most important. Say your inference API is mission-critical for serving real-time predictions. You assign it a high priority class, so if the cluster gets tight on resources, Kubernetes will evict lower-priority training pods first to keep your API online. It’s like giving your VIP pods first dibs on lifeboats. PodDisruptionBudgets are your safety net during chaos, like node maintenance or unexpected failures.

They let you set rules, like “always keep at least two pods of my inference API running, no matter what.” So even if a node goes down or you’re scaling things around, Kubernetes respects your PDB and ensures your critical services don’t drop to zero.
By combining these tools—probes, limits, node pools, priority classes, and PDBs—you’re basically building a bulletproof cluster that can handle the worst-case scenarios. Your training jobs can go wild, your buggy code can misbehave, or your network can choke, but your critical inference API? It stays up, serving predictions like a champ, no matter what chaos is happening in the background.

Data Management in Kubernetes for AI

AI workloads thrive on data, and Kubernetes isn’t magically going to manage terabytes for you. But it does integrate beautifully with cloud storage. On Azure Kubernetes Service (AKS), you can mount Azure Blob Storage or Azure Files directly into pods. On IBM Cloud Kubernetes Service (IKS), you can connect to IBM Cloud Object Storage buckets. This way, your training pod doesn’t need to download datasets manually — they’re available like a mounted disk. Even better, you can integrate with distributed file systems like CephFS or GlusterFS for faster throughput. Without this, your GPUs might sit idle, waiting for data, which is like having a Ferrari but keeping it stuck in traffic.

Handling AI Model Updates Seamlessly

AI models aren’t static — they evolve. You train a model, deploy it, realize it needs fine-tuning, retrain, redeploy. Without Kubernetes, updating models means downtime. With Kubernetes rolling updates, you can replace old model pods with new ones without breaking live traffic. Even better, you can use Canary Deployments or Blue-Green Deployments to test new models on a small slice of traffic before going all-in. Imagine rolling out GPT-5 inference, testing it on 5% of users, and only upgrading once it proves stable. That’s the magic of Kubernetes in action

Monitoring and Observability for AI Clusters

Now, let's talk about observability and monitoring for AI clusters on Kubernetes. Without enough visibility, managing those clusters is like operating a Formula 1 car while wearing a blindfold; you're going to crash, and it won't look good. You need to keep an eye on metrics, logs, traces, and most importantly, GPU performance because AI workloads, particularly those involving GPUs, can be resource hogs and picky. Kubernetes provides the framework, but in order to see what's going on underneath the scenes, you must plug in the appropriate tools.

First, for metrics, you should use Prometheus and Grafana. Prometheus collects and saves data in a time-series database from your cluster, such as CPU usage, memory pressure, or pod restarts. You can quickly determine whether your nodes are choking or your pods are thrashing by looking at the sleek, customisable dashboards that Grafana creates from that data. You must monitor GPUs in addition to CPU and memory when working on AI workloads. NVIDIA's DCGM (Data Centre GPU Manager) Exporter can help with that. It connects to Prometheus and retrieves comprehensive GPU statistics, including temperature, utilisation percentage, and memory usage. To determine whether a training job is using up all of the VRAM or whether a node's GPUs are getting so hot that they are frying an egg, you can graph everything in Grafana.

Then there are logs, which are your treasure collection of information for determining the cause of any issues your AI model may have. You can collect, store, and search logs from all of your pods with tools like OpenSearch or the ELK stack (Elasticsearch, Logstash). You may review the logs to identify the one malicious bug or wrongly configured parameter causing your PyTorch job to crash or your inference server to throw errors. To keep you from getting lost in a sea of text files, Kibana or OpenSearch dashboards make it simple to filter and view log data.

For distributed AI workloads, like multi-node training jobs where data’s flying between pods jaeger steps in for tracing. It tracks requests as they hop across your services, so you can see if a network bottleneck is slowing down your distributed training or if one pod’s taking forever to respond. This is crucial when your model’s split across nodes for parallel processing, and you need to know where the holdup is.

You're not just monitoring with these tools, Prometheus + Grafana for metrics, ELK/OpenSearch for logs, Jaeger for tracing, and cloud-native solutions like Azure Monitor or Sysdig, you're staying ahead of the curve. They let you catch issues before they snowball, whether it’s a GPU overheating, a pod stuck in a crash loop, or a network glitch tanking your training speed. It keeps your race car on the track and off the wall, much like a pit crew for your AI cluster.

Security in AI Kubernetes Deployments

The greatest advantage is that Kubernetes isn't dependent on any one cloud. You can use on-premise GPU rigs, run AKS for some workloads, and IKS for others. Cluster management across multiple environments is possible with Azure Arc or Federated Kubernetes (KubeFed). This implies that while inference APIs run on Azure for worldwide distribution, your AI training may take place on IBM's GPU cluster. The glue that turns multi-cloud AI into something useful rather than unpleasant is Kubernetes.

Kubernetes in Azure for AI Deployments

Let's look at how Microsoft's Azure Kubernetes Service (AKS), with all the features for scalability, storage, and flexibility, makes AI workloads appear simple to use. AKS functions as a supercharged control centre for your AI applications, and when combined with Azure's ecosystem, it simplifies the deployment and management of things like inference APIs and training jobs.

You can quickly start training jobs on AKS clusters with Azure Machine Learning (Azure ML) integration. Consider Python dependencies, model frameworks like PyTorch or TensorFlow, and even GPU support as Azure ML takes care of the laborious task of configuring your environment. It schedules everything nicely if you simply point it at your AKS cluster and specify that "I need 4 NVIDIA A100s for this deep learning job." Additionally, it has auto-scaling built in, so if your training job starts processing data at a rapid pace, AKS can add nodes or spin up more pods (thanks to Cluster Autoscaler) to keep things running smoothly.

This is where things start to get spicy: Azure Container Instances (ACI) integration with virtual nodes. Imagine that your AKS cluster is fully loaded, with nodes crammed and GPUs screaming. Virtual Node allows you to "burst" additional workloads into serverless containers on ACI without having to worry about it. It's similar to on-demand horsepower rentals without the need to provision additional nodes. You don't miss a beat as your inference or training tasks continue. It's ideal for those erratic spikes, such as when your AI app gains popularity.
Azure Kubernetes Service (AKS) is a Swiss Army knife for deploying and managing large models, enhancing AI workloads through seamless integration with Azure's ecosystem. When you combine AKS with Azure Data Lake, you can handle petabyte-scale datasets (think text, images, or video) that are centrally stored and easily accessed by pods using Blob Storage or Azure Files. This way, you don't have to worry about data shuffles or running out of disc space for real-time inference or training. While Azure Container Instances (ACI) integration with Virtual Node enables you to burst workloads to serverless containers during traffic spikes, Azure Machine Learning (Azure ML) makes it easier to launch training jobs on AKS with GPU support and auto-scaling, keeping your cluster running without issue. Azure Arc provides the same auto-scaling, GPU support, and monitoring as if they were in Azure, allowing you to manage AKS clusters anywhere—on-premises, AWS, Google Cloud, or your own data center—for hybrid or multi-cloud freedom. No matter where your AI workloads are located, this set-it-and-forget-it solution is capable of handling massive datasets, unexpected spikes, and dispersed clusters.

Kubernetes in IBM Cloud for AI Deployments

Let's discuss why, despite not receiving as much attention as AWS or Azure, IBM Cloud Kubernetes Service (IKS) is a hidden gem for AI deployments. With some serious tricks up its sleeve, such as strong GPU support for NVIDIA Tesla cards, IBM Cloud Satellite, and tight integration with Watson AI services, IKS is designed to handle AI workloads. Additionally, IBM's emphasis on compliance makes it a stronghold for sensitive AI deployments if you work in a regulated sector like healthcare or finance.

IKS provides a managed Kubernetes environment that integrates well with Watson AI services, enabling you to install models on your cluster for applications such as generative AI, natural language processing, and predictive analytics. Watson's tools, such as watsonx.ai, use Kubernetes for orchestration and scaling while making it simple to train, optimise, and serve models. For instance, you can launch a pod that runs a fraud detection model or a chatbot driven by Watson, and IKS makes sure it has the CPU, memory, and GPUs it requires. In relation to GPUs, IKS supports NVIDIA Tesla cards (such as the V100 or A100), which are powerful tools for AI training and inference. With the NVIDIA Device Plugin installed, IKS effortlessly schedules your workloads on GPU-enabled nodes, and you can request these GPUs in your pod specs. For demanding inference tasks, such as processing medical imaging data or making real-time predictions for financial risk models, this is ideal.

A powerhouse for AI deployments, IBM Cloud Kubernetes Service (IKS) quietly shines with Watson AI integration, support for NVIDIA Tesla GPUs, and IBM Cloud Satellite, enabling clusters to run anywhere—on-premises, on the edge, or across clouds—using a single Kubernetes control plane. This makes it ideal for low-latency IoT analytics or HIPAA-compliant healthcare AI. The IBM Cloud Security and Compliance Centre, encryption, and confidential computing make up its ultra-secure infrastructure, that ensures compliance for sensitive financial or medical AI workloads. Watson's governance tools also maintain models' fairness and auditability. IKS provides hybrid flexibility with strong performance, making it a secret weapon for quick, scalable, and secure AI.

The Future: Kubernetes + AI Operators

For Kubernetes, operators function similarly to intelligent assistants. You install an operator who is capable of managing training jobs rather than manually configuring them. You have the Kubeflow Operator for machine learning pipelines, the Ray Operator for distributed AI, and even the NVIDIA GPU Operator for runtime and driver management. By doing this, Kubernetes transitions from "manual setup" to "self-driving AI infrastructure." Imagine that the operator takes care of all the unpleasant details when you say, "I want to train this model with 100 GPUs." We are entering that future.

Wrapping It Up: Why Kubernetes is the AI Deployment Backbone

Deployments of AI are unpredictable, expensive to execute, and messy. While it does not completely remove the mess, Kubernetes manages it, scales it, fixes it, and makes it sustainable. The foundation of AI is Kubernetes, regardless of whether you're using Azure, IBM Cloud, or even a hybrid multi-cloud configuration. You won't be afraid of AI deployments once you understand pods, nodes, scaling, storage, monitoring, and security. Rather, you will take pleasure in seeing your models grow to thousands of users without experiencing any issues. At that point, Kubernetes becomes your AI wingman and ceases to be merely "container orchestration."

A Lightweight Big Data Stack for Python Engineers

Ranga Bashyam G — Mon, 30 Jun 2025 15:42:29 +0000

Hi and greetings to the dev.to community!

This is my very first blog here, and I'm excited to share my thoughts and experiences with you all.

Over the years, I've primarily worked with Python-based technologies, so I’m quite comfortable with tools and libraries like Flask, Apache Airflow (DAGs), Pandas, PyArrow, and DuckDB. While I haven’t focused much on tools like PySpark or Hadoop, I’ve been deeply involved in handling large-scale data using Parquet files, performing data cleaning, designing robust pipelines, and deploying data workflows in a modular and scalable way.

Though my core expertise lies in Artificial Intelligence and Data Science, I’ve also taken on the role of a Data Engineer for several years, working across backend systems and real-time pipelines.

I'm happy to be part of this community, and I look forward to sharing more technical insights and learning from all of you.

Let’s dive into the world of Data Engineering!

What is Data Engineering?

Data Engineering is a critical discipline within the broader data ecosystem that focuses on building and maintaining the architecture, pipelines, and systems necessary for the collection, storage, and processing of large volumes of data. It is the foundation that supports data science, analytics, and machine learning operations. At its core, data engineering deals with designing robust and scalable systems that move data from various sources into forms that are usable by downstream applications.

A data engineer is responsible for ensuring that data is not only collected but also cleaned, structured, and made available in a timely manner. This involves a strong understanding of databases, distributed systems, scripting, and workflow orchestration tools. It is not a one-size-fits-all role; depending on the scale and nature of the organization, a data engineer might wear many hats—ranging from data ingestion and transformation to cloud infrastructure setup and pipeline optimization.

ETL and ELT Pipelines

A central task in data engineering is the implementation of ETL (Extract, Transform, Load) or ELT (Extract, Load, Transform) pipelines.

ETL is the traditional method where data is first extracted from source systems such as transactional databases, APIs, or flat files. It is then transformed—cleaned, aggregated, and reshaped—before being loaded into a destination system, often a data warehouse. This approach works well when transformation is done outside the warehouse, especially when transformation logic is complex or the warehouse is compute-constrained.

On the other hand, ELT has gained popularity with the rise of cloud-native data warehouses like Snowflake, BigQuery, and Redshift. In ELT, raw data is loaded directly into the warehouse, and all transformations are performed post-load. This method benefits from the massive parallelism and compute power of modern data warehouses and keeps raw data accessible for reprocessing.

Working with Data Lakes (IBM Cloud Object Storage)

A data lake is a centralized repository designed to store all structured, semi-structured, and unstructured data at scale. Unlike a data warehouse that requires predefined schemas, data lakes support schema-on-read, allowing more flexibility for exploration and modeling. Schema definitions are more important to maintain the folder structure, it is even more important in Parquet files to define schema in a proficient way.

IBM Cloud Object Storage (COS) is a popular choice for building data lakes, especially in hybrid cloud environments. It offers durability, scalability, and support for open data formats like Parquet and ORC. Engineers often use IBM COS as a staging ground for raw and processed data before it is ingested into analytics or machine learning workflows.

In practice, data engineers use services like IBM COS to store logs, streaming data, and backup files. The stored data is accessed using Python libraries such as boto3 or ibm_boto3.

Accesssing COS Bucket technically:

import ibm_boto3
from ibm_botocore.client import Config

cos = ibm_boto3.client("s3",
    ibm_api_key_id="API_KEY",
    ibm_service_instance_id="SERVICE_ID",
    config=Config(signature_version="oauth"),
    endpoint_url="https://s3.us-south.cloud-object-storage.appdomain.cloud"
)

# List files in a bucket
cos.list_objects_v2(Bucket="my-bucket")['Contents']

Pandas, SQL, Parquet, DuckDB, and PyArrow

Data engineering often involves working with various tools and formats for transformation and storage. Here’s how these technologies fit into a typical stack:

Pandas: A go-to Python library for data manipulation, ideal for small to medium datasets. While not optimal for big data, it is excellent for rapid prototyping and local transformations.
SQL: Structured Query Language remains a cornerstone of data transformations. Whether running in PostgreSQL, Snowflake, or embedded systems like DuckDB, SQL is used to clean, join, filter, and aggregate data.
Parquet: A columnar storage format that supports efficient querying and compression. It is widely used for storing processed datasets in data lakes due to its performance benefits in analytics.
DuckDB: An in-process SQL OLAP database that can query Parquet files directly without loading them into memory. It allows data engineers to write complex SQL queries on large datasets stored in files, making it excellent for fast, local experimentation.
PyArrow: A Python binding for Apache Arrow, enabling efficient serialization of data between systems. PyArrow is used under the hood by many libraries (including Pandas and DuckDB) to enable zero-copy reads and writes, boosting performance.

Together, these tools form a powerful suite for local and scalable data processing. A typical use case might involve reading a Parquet file from IBM COS using PyArrow, manipulating it with Pandas or DuckDB, and exporting it to a data warehouse via an ELT pipeline.

Why DuckDB is a Better Fit Than Modin or Vaex for Large-Scale Data Processing

Using DuckDB over Modin or Vaex is often a more robust and scalable approach when working with large datasets—particularly in Parquet format. DuckDB is highly efficient at processing queries directly on disk without loading the full dataset into memory. Attempting to perform complex operations like correlation or aggregations directly on massive in-memory dataframes is not only memory-intensive but can also be error-prone or slow. A better pattern is to convert the DataFrame to Parquet and use DuckDB to query and process the data efficiently. This offers both speed and scalability in a single-node environment.

Moreover, complex operations like joins, filters, aggregations, and correlations are optimized inside DuckDB’s vectorized execution engine. It handles query planning and execution more efficiently than the implicit operations in Modin or Vaex, which often delegate tasks to backends like Dask or rely on caching in RAM.

Some basic code level Implementations

Certainly! Here's a concise yet informative overview of the NYC Yellow Taxi Trip Data dataset you're using:

Dataset Overview: NYC Yellow Taxi Trip Records (2023)

The NYC Yellow Taxi Trip Dataset is a public dataset provided by the New York City Taxi & Limousine Commission (TLC). It contains detailed records of individual taxi trips taken in NYC, collected directly from the taxi meters and GPS systems.

For this example, we're using data from:

January 2023
Parquet File URL:
https://d37ci6vzurychx.cloudfront.net/trip-data/yellow_tripdata_2023-01.parquet

Schema (Sample Columns)

Column Name	Description
`vendorid`	ID of the taxi provider (1 or 2)
`tpep_pickup_datetime`	Timestamp when the trip started
`tpep_dropoff_datetime`	Timestamp when the trip ended
`passenger_count`	Number of passengers
`trip_distance`	Distance of the trip in miles
`ratecodeid`	Rate type (standard, JFK, Newark, etc.)
`store_and_fwd_flag`	If trip record was stored and forwarded due to loss of signal
`payment_type`	Type of payment (credit card, cash, etc.)
`fare_amount`	Base fare of the trip
`extra`	Additional charges (e.g., peak hour)
`mta_tax`	NY MTA tax
`tip_amount`	Tip paid by passenger
`tolls_amount`	Tolls charged during trip
`improvement_surcharge`	Fixed surcharge
`total_amount`	Total charged to the passenger

Size and Volume

~7 to 10 million rows per month
File size ranges from 300MB to 1GB+ in Parquet format
Data is stored in a columnar format, making it efficient for analytics

We’ll cover:

Trip duration calculation
Average fare per distance bucket
Peak hour identification

Basic Configs:

import duckdb

# Connect DuckDB
con = duckdb.connect()

parquet_url = "https://d37ci6vzurychx.cloudfront.net/trip-data/yellow_tripdata_2023-01.parquet"

1. Trip duration calculation:

# Calculate average trip duration and fare per hour of the day
query1 = f"""
    SELECT 
        EXTRACT(hour FROM tpep_pickup_datetime) AS pickup_hour,
        COUNT(*) AS total_trips,
        AVG(DATE_DIFF('minute', tpep_pickup_datetime, tpep_dropoff_datetime)) AS avg_trip_duration_min,
        AVG(total_amount) AS avg_total_fare
    FROM read_parquet('{parquet_url}')
    WHERE 
        tpep_dropoff_datetime > tpep_pickup_datetime 
        AND total_amount > 0
    GROUP BY pickup_hour
    ORDER BY pickup_hour
"""

result1 = con.execute(query1).fetchdf()
print("\nTrip Duration & Fare by Hour of Day:")
print(result1)

Output:

2.Average fare per distance bucket

# Bucket trip distances and calculate average fare per bucket
query2 = f"""
    SELECT 
        CASE 
            WHEN trip_distance BETWEEN 0 AND 1 THEN '0-1 mi'
            WHEN trip_distance BETWEEN 1 AND 3 THEN '1-3 mi'
            WHEN trip_distance BETWEEN 3 AND 5 THEN '3-5 mi'
            WHEN trip_distance BETWEEN 5 AND 10 THEN '5-10 mi'
            ELSE '>10 mi'
        END AS distance_bucket,
        COUNT(*) AS num_trips,
        AVG(total_amount) AS avg_fare
    FROM read_parquet('{parquet_url}')
    WHERE total_amount > 0 AND trip_distance > 0
    GROUP BY distance_bucket
    ORDER BY num_trips DESC
"""

result2 = con.execute(query2).fetchdf()
print("\nFare vs Distance Buckets:")
print(result2)

Output:

3. Vendor wise earnings

# Vendor-wise Earnings
query3 = f"""
    SELECT 
    vendorid,
    COUNT(*) AS num_trips,
    SUM(total_amount) AS total_revenue,
    AVG(total_amount) AS avg_fare
FROM read_parquet('{parquet_url}')
WHERE total_amount > 0
GROUP BY vendorid
ORDER BY total_revenue DESC
LIMIT 5
"""

result3 = con.execute(query3).fetchdf()
print("\nVendor-wise Earnings:")
print(result3)

Output:

Orchestrating Pipelines with Apache Airflow

Once data pipelines are defined, orchestrating them becomes a challenge—especially when multiple tasks need to be scheduled, retried on failure, and monitored. Apache Airflow solves this by allowing engineers to define workflows as Directed Acyclic Graphs (DAGs) using Python.

Airflow supports task dependencies, scheduling, retries, logging, and alerting out-of-the-box. Each task in Airflow is executed by an operator. For instance, a PythonOperator might run a transformation script, while a BashOperator could trigger a shell script to ingest data.

A typical Airflow pipeline might look like this:

Pull data from an API using a Python script.
Load the raw data into IBM Cloud Object Storage.
Run a DuckDB transformation on the stored Parquet files.
Export the clean data into a data warehouse.
Trigger a Slack or email notification on completion.

Airflow's extensibility, combined with its scheduling and monitoring features, makes it the default choice for modern data engineering teams.

Conclusion

Data Engineering is both a foundation and a force multiplier for modern analytics and AI systems. From building reliable ETL/ELT pipelines to managing petabytes of data in cloud storage, the role demands a mix of software engineering, data modeling, and system design skills.

As the data landscape evolves, so do the tools and techniques. Technologies like DuckDB and PyArrow are transforming how we process data locally, while orchestrators like Airflow and cloud platforms like IBM COS make it easier to scale and automate data workflows. A successful data engineer needs to stay deeply technical, understand the underlying principles, and always design systems with scalability, reliability, and maintainability in mind.