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

MLOps / AI Infrastructure Learning Path

Welcome

This is your personalized learning environment for becoming an MLOps / AI Infrastructure Engineer.

Each section includes:

  • Introduction - What you're learning and why it matters
  • Tasks - Hands-on work to complete
  • AI Assistant - Chat with your local LLM for guidance
  • Verification - Questions to confirm understanding

Your 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