Week 4: Containerization, Kubernetes in the AI Era

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Hey Inner Circle,

This week we’re switching the format a bit — straight into the topic and what you should know (and practice).

By now, you’ve already gone through the building blocks: networking, IAM, storage types, and infrastructure provisioning. But the real shift in modern cloud engineering starts with automation + software delivery.

That’s where CI/CD pipelines and containerization step in.

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Containerization: The Big Shift

Before containers, we had Virtual Machines (VMs) → each running its own OS, libraries, and apps. They worked, but were heavy, slow to scale, and resource-hungry.

Then came containers → lightweight, portable units sharing the host OS kernel, making application deployment faster and more efficient.

Problem? When containers multiplied across environments (think hundreds or thousands), managing them became a nightmare.

Enter Kubernetes — the orchestrator that automated deployment, scaling, and management of containers.

Today, Kubernetes isn’t just “container management” — it’s practically a cloud operating system, powering everything from fintech apps to AI pipelines.

That’s all the history you need for NOW!

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Kubernetes Learning Roadmap (3 Phases)

Phase 1 – Foundation (Architecture)

Understand the skeleton of how Kubernetes works.

• Cluster components → Control plane (manages objects) + worker nodes that run and manage workloads.

• etcd → Distributed key-value store holding all cluster state and configuration.

• API Server → Central hub that handles all requests to the cluster.

• Scheduler → Assigns pods to the best available nodes based on resources and rules.

•  Kubelet & kube-proxy → Kubelet ensures containers run; kube-proxy routes traffic in/out of pods.

Resources to review:

• Youtube - Kubernetes Architecture

• Github - Kubernetes Resources

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Phase 2 – Core Objects (The Building Blocks)

These are the everyday primitives you’ll work with.

• Pods → Smallest deployable unit, wrapping one or more containers.

• Deployments → Manage pod scaling and rolling updates.

• Services → Provide stable networking and discovery for pods.

• ConfigMaps & Secrets → Store app configs and sensitive data securely.

• Namespaces → Logical partitions to isolate workloads within a cluster.

• RBAC → Role-based access control for fine-grained permissions.

Resources to Review

• Youtube - Kubernetes Crash Course

• Github - K8s Project Examples

Expected Outcome: Comfort with kubectl basics — deploy, scale, roll back, and expose a simple app.

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Phase 3 – Advanced (Control & Extensibility)

Once you know the basics, step into deeper K8s automation.

• Controllers → Continuously reconcile actual vs desired state of resources.

• Operators → App-specific controllers that automate complex tasks.

• CRDs (Custom Resource Definitions) → Extend Kubernetes with new resource types.

• Networking policies & Ingress controllers → Secure and manage traffic inside/outside the cluster.

• CI/CD for Containers & K8s Deployment Strategies → Automate application delivery with pipelines and safe rollout patterns (rolling, blue-green, canary)

There’s more to it, but we’ll cross the bridge when we reach there..

Resources to Review:

• Youtube:

  • Operators

  • Custom Resources

  • CI/CD Pipeline with K8s

• Github: Kubernetes Advanced

Expected Outcome: Understand how Kubernetes adapts to complex workloads like AI/ML pipelines and large-scale production apps.

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Why Kubernetes Matters in the AI Era

1. AI models need scalable GPU/TPU workloads → K8s can schedule these efficiently. Both training models + serving models..

2. ML pipelines (training → serving → monitoring) run smoother when containerized.

3. Cloud providers provides K8s-native AI services (Vertex AI on GCP, SageMaker on EKS, Azure ML on AKS).

4. Multi-tenant AI infra → simplified with namespaces, quotas, and policies.

In short: If cloud is the backbone, Kubernetes is the nervous system — and AI workloads are pushing it to evolve even faster.

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Common Practice Grounds:

1. KodeKloud.com

   • Free practice labs

   • Solution Videos

   • Community Support

2. killercoda.com

   • Interactive K8s environment

   • Real-time feedback

3. play-with-k8s.com 

   • 4-hour free clusters

   • Multi-node setup

4. https://kubernetes.io/docs/tutorials

   • Official practice scenarios

   • Updated regularly

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What’s Next?

Next week, we’ll merge Week 5 + Week 6 into a combined session to cover CI/CD pipelines in detail along with cloud security and devsecops worflows.

See you next week,

— Vishakha

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