Kubernetes for MLOps

Master container orchestration for production ML workloads

Quick Start

1. Read the conceptual guides in this folder
2. Practice with labs in ../../../module-01/k8s/

Learn:  docs/module-01/k8s/       →  Theory and concepts
Do:     module-01/k8s/             →  Hands-on practice

Learning Objectives

By the end of this section, you will be able to:

• Understand why Kubernetes is essential for production MLOps workloads
• Set up a local Kubernetes development environment
• Deploy and manage containerized applications using Kubernetes
• Configure networking, storage, and load balancing
• Manage application configuration and secrets
• Use Helm for package management
• Deploy ML models as scalable microservices

Prerequisites

• Completion of Docker fundamentals
• Basic Linux command line familiarity
• Understanding of microservices architecture
• YAML syntax basics

Study Path

1. Overview

Read: Overview - Why Kubernetes for MLOps

2. Key Concepts

Core Objects:

• Overview
• Object Model
• Namespaces
• Pods
• Labels & Selectors

Workloads:

• Overview
• ReplicaSet
• Deployment
• StatefulSet
• DaemonSet
• Job
• CronJob

Storage:

• Overview
• Storage Classes
• PersistentVolumes
• PersistentVolumeClaims

Configuration:

• Overview
• ConfigMaps
• Secrets

Network:

• Overview
• Services
• Service Discovery
• Ingress
• Ingress Gateway

3. Architecture

Read: Architecture Overview

4. Helm

Read: Helm Package Manager

5. Monitoring

Read: Monitoring & Observability

Kubernetes Versions

This module uses Kubernetes v1.32 "Penelope" (current stable release as of 2025).

Tool Versions Used

Tool	Version	Purpose
kubectl	v1.32.x	Kubernetes CLI
minikube	v1.37.0+	Local K8s cluster
kind	v0.24.0+	Docker-based K8s
helm	v3.16.x	Package manager

Why This Module Matters

Docker is Great, But...

After completing Docker training, you can:

• Build and run containers locally
• Use Docker Compose for multi-container apps
• Share images via registries

Why You Need Kubernetes

Production Realities:

• What happens when a container fails?
• How do you scale to handle 10x traffic?
• How do you deploy without downtime?
• How do you manage secrets securely?
• How do you run across multiple nodes/servers?

Kubernetes Solves These By:

• Auto-restart failed containers
• Scale applications automatically
• Rolling updates with zero downtime
• Built-in secrets management
• Multi-node orchestration

MLOps-Specific Benefits

• Model Serving: Deploy ML models as scalable microservices
• Batch Jobs: Run training jobs as Kubernetes Jobs/CronJobs
• Resource Management: Allocate GPU/TPU resources for ML workloads
• A/B Testing: Run multiple model versions simultaneously
• Hybrid Cloud: Run on-prem, AWS EKS, GCP GKE, Azure AKS

Quick Reference

Essential kubectl Commands

# Cluster info
kubectl cluster-info
kubectl version

# Pod operations
kubectl get pods
kubectl describe pod <pod-name>
kubectl logs <pod-name>
kubectl exec -it <pod-name> -- /bin/bash

# Deployment operations
kubectl get deployments
kubectl apply -f deployment.yaml
kubectl rollout status deployment/<name>
kubectl scale deployment/<name> --replicas=3

# Service operations
kubectl get services
kubectl port-forward <pod-name> 8080:80

# Debugging
kubectl logs <pod-name> --previous
kubectl describe pod <pod-name>
kubectl get events

Best Practices Summary

1. Always use declarative YAML - Don't use imperative commands
2. Set resource requests/limits - Prevent resource starvation
3. Use liveness and readiness probes - Enable self-healing
4. Namespaces separation - Dev/staging/prod isolation
5. Secrets management - Never commit secrets to git
6. Health checks - Always define startup, readiness, liveness probes
7. Rollback strategy - Keep deployment history
8. Monitor everything - Metrics, logs, and traces

Common Pitfalls

Pitfall	Why It's Bad	Solution
Running as root	Security risk	Use security contexts
No resource limits	Noisy neighbors	Set requests/limits
:latest tag	Unpredictable updates	Pin specific versions
Hardcoded config	Not portable	Use ConfigMaps/Secrets
Monolithic pods	Poor scaling	One container per pod
Ignoring probes	Failed pods restart forever	Add health checks

Additional Resources

Official Documentation

• Kubernetes Documentation
• kubectl Command Reference
• API Reference

Books

• Mastering Kubernetes (4th Edition) - Our primary reference
• Kubernetes Up & Running

Communities

• Kubernetes Slack
• r/kubernetes
• Stack Overflow - Kubernetes Tag

Next Steps

After completing this section:

1. Practice with real-world scenarios in ../../../module-01/k8s/
2. Deploy an ML model as a Kubernetes service
3. Explore Kubernetes monitoring and observability
4. Learn about GitOps with ArgoCD/Flux

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Practice Labs: ../../../module-01/k8s/

Return to: Module 1 | Study Guide