MLOps Learning

Roadmap

Phase1 Cloud Iac

IAM Basics
IAM Roles & Service Accounts
S3 Fundamentals
VPC Fundamentals
EC2 Fundamentals
Introduction to Terraform
Terraform State Management
Terraform Modules

Phase2 Containers K8s

Docker Deep Dive
Kubernetes Core Concepts
Kubernetes Hands-On with Kind
Kubernetes YAML Manifests
EKS Cluster Setup with Terraform
GitHub Actions Fundamentals
Complete CI/CD Pipeline

Phase3 Ml Lifecycle

ML Fundamentals for MLOps
MLflow Experiment Tracking
MLflow Model Registry
Model Serving with FastAPI
Airflow Fundamentals
ML Pipeline with Airflow

Phase4 Capstone

Capstone Architecture Design
Capstone Infrastructure Setup
Deploy MLflow to Kubernetes
Capstone Training Pipeline
Capstone Model Serving
Monitoring and Observability
Data and Model Drift Detection
Capstone CI/CD Integration
Documentation and Portfolio

Phase2 Containers K8s

Docker Deep Dive

Introduction

You already have Docker installed and probably know the basics — pull an image, run a container. This section goes deeper into the skills you need for production MLOps work: writing efficient Dockerfiles, multi-stage builds, and understanding how images work.

Why This Matters

In MLOps, almost everything runs in containers: - Training jobs run in containers - Model inference runs in containers - Airflow tasks run in containers - Your entire Kubernetes cluster runs containers The difference between a 2GB image that takes 10 minutes to build and a 200MB image that builds in 30 seconds matters enormously at scale. Understanding Docker deeply means faster CI/CD, cheaper storage, and quicker deployments.

Tasks

Write a Dockerfile for a simple Python FastAPI app

Build the image and note its size

Understand layers — modify the app code and rebuild, observe what's cached

Reorder the Dockerfile to maximize cache hits (dependencies before code)

Convert to a multi-stage build — separate build and runtime stages

Compare image sizes before and after multi-stage

Use a slim base image (python:3.11-slim) instead of full python:3.11

Add a .dockerignore file to exclude unnecessary files

Run the container with environment variables and port mapping

Hint: docker run -e VAR=value -p 8000:8000 myimage

Use docker-compose to define the app with its configuration

Push your image to Docker Hub or AWS ECR

Check Understanding

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