Advanced Kubernetes Explained

Hi Inner Circle!

Welcome to this week's edition.

Today we're breaking down Advanced Kubernetes ~ and why in the AI era, Kubernetes has become an essential skillset.

Review this before your next interview!!

Also why just knowing Pods and Deployments is not enough.

We'll cover:

→  Why Kubernetes is booming in AI

→  Why advanced features actually exist

→  The domains mapped

→  Hands-on projects you can build

Before we begin... a big thank you to today's partner ~ Anyshift

Your monitoring tells you what broke. It doesn't tell you why.

Anyshift maps every dependency across your cloud, K8s, and codebases ~ live, always up to date. When something breaks, Annie (your AI SRE) traces the full dependency chain and shows your team the root cause in seconds, not the usual twenty-minute archaeology session across five tools.

Check out Annie at https://www.anyshift.io/main

Now let’s dive in…

AI workloads changed infrastructure.

Traditional applications needed:

→  Containerized services

→  Predictable scaling

→  Standard networking & observability

AI systems need:

→  GPU-aware scheduling

→  Multi-node distributed training

→  High-throughput networking

→  Massive dataset movement

→  Long-running jobs with checkpointing

→  Deep observability and a lot more…. (trust me)

Kubernetes became the control plane for all of it.

Knowing Pods, Services, and Deployments makes you familiar.

Understanding how these accelerator based clusters behave under pressure makes you valuable.

Advanced Kubernetes exists because things broke at scale.

Let's unpack that.

1. Cluster Management & Scaling

Why it exists: Static clusters don't survive AI workloads.

Cluster Autoscaler

Manual scaling failed.. humans are slow, workloads are not. Cluster Autoscaler detects unscheduled pods, adds nodes automatically, and removes underutilized ones.

→  Without it → bottlenecks

→  With it → elasticity

KEDA (Kubernetes Event-Driven Autoscaling ~ open source)

CPU-based scaling wasn’t enough for modern workloads. Many AI systems scale based on queue length, event streams, or message backlog… not CPU usage. KEDA exists to enable event-driven autoscaling, allowing applications to scale up or down based on external triggers rather than just resource consumption.

GPU Scheduling

GPUs are expensive and limited resources.. you can’t share or oversubscribe them the way you do with CPU or memory. AI workloads often require dedicated GPU access, sometimes multiple GPUs together, and in certain cases even specific hardware layouts.. The default Kubernetes scheduler wasn’t designed with these constraints in mind. That’s why GPU operators and specialized scheduling mechanisms exist ~ to ensure GPUs are allocated properly, efficiently, and without resource conflicts in AI-heavy environments.

2. Observability & Reliability

Why it exists: Distributed systems hide failures.

Service A calls B. B calls C. C times out. Where did it break? Logs alone couldn't answer that.

Prometheus

Traditional monitoring used fixed limits (like “alert if CPU > 80%”), which often caused too many unnecessary alerts. Prometheus was built to track metrics over time, understand what “normal” looks like, and trigger alerts based on real patterns instead of just static numbers.

OpenTelemetry

Before it, logs, metrics, and traces lived separately ~ debugging required guesswork. OpenTelemetry unified telemetry because distributed debugging required correlation. You need to understand the journey of a request.

Chaos Engineering

Chaos engineering is the practice of intentionally injecting failures into your system to test how it behaves under stress.

It helps teams identify weaknesses early and build resilient, production-ready infrastructure before real outages happen.

If you don't inject failure intentionally, production will do it for you.

3. GitOps & Platform Engineering

Why it exists: Manual kubectl doesn't scale across teams.

Engineer A deploys manually. Engineer B edits config directly. Cluster drifts from source code. Now nobody knows the truth.

ArgoCD / Flux

ArgoCD and Flux are GitOps controllers for Kubernetes.

They continuously compare:

What’s declared in Git vs What’s actually running in your cluster

And if there’s a difference; they fix it automatically. That process is called reconciliation.

Helm

Copy-pasting YAML can break at scale. When you’re managing 5 environments, 100+ services, and small configuration differences between each one, maintaining separate YAML files becomes messy and error-prone. Templating becomes essential ~ and that’s where Helm comes in. It lets you create reusable, parameterized templates so you can manage complex deployments cleanly and consistently across environments.

4. Networking & Traffic Control

Why it exists: Microservices created networking complexity.

You need canary deployments, TLS termination, traffic splitting, and mTLS between services. ClusterIP wasn't enough.

Ingress & Gateway API

They exist because external routing across 1000s of services must be centralized and controlled.

Service Mesh

As systems moved to microservices, teams started adding things like retry logic, security rules, and traffic controls directly inside each service. Over time, this became messy and hard to maintain ~ every service handled networking and security differently.

Service meshes were created to solve this. They move concerns like traffic shaping, mutual TLS (mTLS), observability, and circuit breaking out of application code and into the infrastructure layer

5. Workload Optimization

Why it exists: Resource waste is expensive.

Over-request memory → wasted cost. Under-request → OOMKilled.

HPA / VPA (Horizontal/Vertical POD Autoscaler)

HPA scales replicas based on load. VPA adjusts resource requests dynamically. They exist because static resource allocation failed.

Node Affinity & Taints

Not all nodes in a cluster are the same. Some have GPUs, some are high-memory, and some may be cheaper spot instances. AI jobs should run on accelerator nodes, and critical databases shouldn’t run on unstable spot nodes. Affinity and scheduling rules exist to make sure workloads are placed on the right machines.

6. ML Pipelines & Experimentation

Why it exists: Running ML workloads on Kubernetes requires more than just pods ~ you need pipeline orchestration, experiment tracking, and model management.

Kubeflow

Training a model once is easy. Managing reproducible pipelines, distributed training jobs, hyperparameter tuning, and model serving at scale is not. Kubeflow exists because ML teams needed a Kubernetes-native platform that handles the full ML lifecycle <from data prep to model deployment> without leaving the cluster.

→  Pipelines: orchestrate multi-step ML workflows

→  Training Operators: run distributed TensorFlow, PyTorch

→  KServe: model serving with autoscaling

MLflow

Teams were losing track of which model version used which dataset, hyperparameters, and code. MLflow exists because ML experimentation without tracking is chaos. It brings observability to the model layer ~ the same way Prometheus brings it to infrastructure.

→  Experiment tracking: log parameters, metrics, artifacts

→  Model registry: version, stage, and promote models

→  Deployment: serve models via REST API

Kubeflow orchestrates your ML pipelines on Kubernetes.

MLflow tracks what happened inside each run.

Together they give you reproducibility at scale.

7. Security & Governance

Why it exists: Multi-team clusters create complexity.

A developer deploys a privileged container. Now the node is compromised.

Pod Security Admission

Default Kubernetes was too permissive. Security policies enforce no privilege escalation, restricted capabilities, and controlled host access.

OPA / Kyverno ~ Policy Engines

RBAC controls access. Policy engines control behavior. They exist so that security can be declarative and enforceable.

8. Storage & Stateful Workloads

Why it exists: Kubernetes was built for stateless apps. But databases exist.

Persistent Volumes(PV)

Containers are ephemeral. Data cannot be. PVs decouple storage from pod lifecycle. So your data doesn’t goes away if a pod is removed.

CSI Drivers (Container Storage Interface.)

It’s a standard that allows Kubernetes to communicate with different storage systems (like AWS EBS, GCP block storage or on-prem storage) in a consistent way. CSI standardized storage integration across all platforms.

Snapshots & Backup

Nodes fail. Disks get corrupted. Accidental deletions happen.

Snapshots and backup systems exist to protect your data and allow you to recover quickly when something goes wrong.

9. Extensibility ~ Architect Level

CRDs & Controllers

Every company eventually needs workflows that default Kubernetes can’t handle ~ like custom scaling based on business metrics, automatic database provisioning per team, or enforcing internal policies. That’s where CRDs and Operators come in. A CRD lets you define your own resource type (for example, AITrainingJob), and an Operator contains the logic that continuously watches that resource and takes action.

In simple terms, you’re teaching Kubernetes new behaviors. This is platform engineering ~ you’re not just using Kubernetes anymore, you’re extending it to fit your organization’s needs.

3 hand-on projects you must try (or at least explore)

1. EKS Scaling with KEDA + Karpenter

Learn how event-driven autoscaling (KEDA) works alongside intelligent node provisioning (Karpenter).

2. Kubernetes Autoscaling Deep Dive (Video Walkthrough)

A practical breakdown of Kubernetes autoscaling strategies. Watch this to solidify your understanding of HPA, event-driven scaling, and cluster-level scaling decisions.

3. End-to-End Kubernetes Three-Tier DevSecOps Project

This is full-stack platform thinking: CI/CD, containerization, security scanning, Kubernetes deployment, and production-style architecture.

Final Thought

Kubernetes isn't hard.

It's layered.

→  Basics make you familiar

→  Advanced domains make you resilient

→  Architect-level thinking makes you rare

In the AI era.. where GPU clusters, distributed training, and high-throughput systems are becoming normal; surface-level knowledge won't differentiate you.

Understanding why these systems exist will.

That's it for this week.

Onwards & Upwards,

-V

P.S.

I am sharing detailed Cloud Roadmaps on Youtube, if you want to dive deeper ~ check them out here.