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AI Infrastructure Engineering

▸ Mod 101 Foundations

MLesson 01: Introduction to ML Infrastructure MLesson 02: Development Environment Setup MLesson 03: ML Infrastructure Basics MLesson 04: ML Frameworks Fundamentals MLesson 05: Cloud Computing for ML Infrastructure MLesson 06: Model Serving Basics MLesson 07: Docker Basics for ML Infrastructure MLesson 08: API Development for ML Services

▸ Exercises

MExercise 01: Environment Verification MExercise 02: Docker Fundamentals for ML MExercise 03: Kubernetes Deployment for ML

▸ Exercise 04 Python Env Manager

▸ Exercise 05 Ml Framework Benchmark

▸ Exercise 06 Fastapi Ml Template Generator

▸ Exercise 07 Cloud Onboarding

▸ Exercise 08 Production Model Serving

▸ Exercise 09 Api Integration Tests

▸ Exercise 10 End To End Mini Project

▸ Labs

MLab 01: Reproducible Python Dev Environment MLab 02: Build, Run, and Inspect Your First Docker Container MLab 03: Stand Up a Local Kubernetes Cluster with kind MLab 04: Serve a Scikit-Learn Model Behind a FastAPI Endpoint MLab 05: Add Prometheus Instrumentation to the FastAPI Service

▸ Quizzes

MModule 01: Foundations of ML Infrastructure - Quiz

MModule 101 — Foundations of ML Infrastructure Resources

▸ Mod 102 Cloud Computing

MLesson 01: Cloud Architecture for ML MLesson 02: AWS for ML Infrastructure MLesson 03: Google Cloud Platform for ML Infrastructure MLesson 04: Azure for ML Infrastructure MLesson 05: Cloud Storage for ML MLesson 06: Cloud Networking for ML MLesson 07: Managed ML Services Comparison MLesson 08: Multi-Cloud & Cost Optimization

▸ Exercises

▸ Exercise 01 Multi Cloud Cost Analyzer

▸ Exercise 02 Cloud Ml Infrastructure

▸ Exercise 03 Disaster Recovery

▸ Exercise 04 Cross Region Replication

▸ Exercise 05 Cloud Networking For Ml

▸ Exercise 06 Managed Ml Services Comparison

▸ Exercise 07 Multi Account Security

▸ Exercise 08 Finops Automation

▸ Labs

MLab 01: Provision an ML-Ready AWS Environment MLab 02: Provision an ML-Ready GCP Environment MLab 03: Provision an ML-Ready Azure Environment MLab 04: Multi-Cloud Deployment with a Single Tool

▸ Quizzes

MModule 02: Cloud Computing - Final Quiz MModule 02: Cloud Computing - Mid-Module Quiz MModule 02 Quiz: Cloud Computing for ML

MModule 02 Resources: Cloud Computing for ML

▸ Mod 103 Containerization

MLesson 01: Docker Introduction MLesson 02: Dockerfiles for ML Applications MLesson 03: Image Optimization MLesson 04: Docker Networking and Volumes MLesson 05: Docker Compose for ML Applications MLesson 06: Container Registries MLesson 07: GPU Support in Docker MLesson 08: Production Best Practices

▸ Exercises

MExercise 01: Build Your First ML Dockerfile MExercise 02: Multi-Container ML Application MExercise 03: GPU-Accelerated ML Container

▸ Exercise 04 Container Security

▸ Exercise 05 Image Optimizer

▸ Exercise 06 Registry Manager

▸ Exercise 07 Buildkit Cache Optimization

▸ Exercise 08 Multi Arch Builds

▸ Exercise 09 Runtime Debugging

▸ Exercise 10 Sbom And Supply Chain

▸ Exercise 11 Ml Container Patterns

▸ Exercise 12 Vulnerability Remediation

▸ Labs

MLab 01: Dockerfile Optimization for ML Workloads MLab 02: Multi-Stage Builds for Slim Production Images MLab 03: Compose a Multi-Service Stack MLab 04: Run a GPU Container with PyTorch CUDA MLab 05: Stand Up a Private Container Registry MLab 06: Security-Scan Images with Trivy

▸ Quizzes

MModule 03 Quiz: Containerization with Docker

MModule 03 Resources: Containerization with Docker

▸ Mod 104 Kubernetes

MLesson 01: Kubernetes Introduction MLesson 02: Kubernetes Architecture MLesson 03: Core Kubernetes Resources MLesson 04: Deployments and Services Deep Dive MLesson 05: Kubernetes Networking and Ingress MLesson 06: Storage and Persistence in Kubernetes MLesson 07: Helm Package Manager MLesson 08: GPU Scheduling in Kubernetes MLesson 09: Monitoring and Troubleshooting Kubernetes

▸ Exercises

▸ Exercise 01 K8s Fundamentals

▸ Exercise 02 Control Plane Tour

▸ Exercise 03 Core Resources Mastery

▸ Exercise 04 K8s Cluster Autoscaler

▸ Exercise 05 Service Mesh Observability

▸ Exercise 06 K8s Operator Framework

▸ Exercise 07 Helm Chart Authoring

▸ Exercise 08 Gpu Scheduling

▸ Exercise 09 Monitoring Troubleshooting

▸ Exercise 10 Multi Cluster Federation

▸ Exercise 11 Custom Operator

▸ Exercise 12 Stateful Postgres

▸ Exercise 13 Cluster Upgrade

▸ Exercise 14 Resource Quotas Multitenancy

▸ Exercise 15 Cluster Cost Optimization

▸ Labs

MLab 01: Your First Pod and Deployment MLab 02: Service Discovery — ClusterIP, NodePort, LoadBalancer MLab 03: ConfigMaps and Secrets MLab 04: Persistent Volumes for Stateful Workloads MLab 05: Package an App with Helm MLab 06: Horizontal Pod Autoscaler under Load MLab 07: NetworkPolicies for East-West Isolation MLab 08: Debug a Broken Deployment from Scratch

MModule 04: Kubernetes Fundamentals - Completion Status

▸ Quizzes

MModule 04 Quiz: Kubernetes Fundamentals

MModule 04 Resources: Kubernetes Fundamentals

▸ Mod 105 Data Pipelines

MLesson 01: Data Pipeline Architecture for ML MLesson 02: Apache Airflow Fundamentals MLesson 03: Advanced Airflow for ML MLesson 04: Data Versioning with DVC MLesson 05: Data Processing with Apache Spark MLesson 06: Streaming Data with Apache Kafka MLesson 07: Data Quality and Validation MLesson 08: Pipeline Monitoring and Error Handling

▸ Exercises

▸ Exercise 01 Pipeline Architecture Design

▸ Exercise 02 Airflow Fundamentals

▸ Exercise 03 Streaming Pipeline Kafka

▸ Exercise 04 Workflow Orchestration Airflow

▸ Exercise 05 Spark Batch Processing

▸ Exercise 06 Data Versioning Dvc

▸ Exercise 07 Data Quality Great Expectations

▸ Exercise 08 Pipeline Monitoring Lineage

▸ Exercise 09 Backfill Strategies

▸ Exercise 10 Etl To Elt Migration

▸ Exercise 11 Lambda Architecture

▸ Exercise 12 Pipeline Cost Optimization

▸ Labs

MLab 01: Run Airflow Locally with docker-compose MLab 02: Author Your First DAG MLab 03: Run a Spark Batch Job Locally MLab 04: Stream Events Through Kafka MLab 05: Version Data and Models with DVC MLab 06: Validate Data with Great Expectations

▸ Quizzes

MModule 05: Data Pipelines - Quiz

MModule 05: Data Pipelines - Resources

▸ Mod 106 Mlops

MLesson 01: Introduction to MLOps MLesson 02: MLflow Experiment Tracking MLesson 03: Model Registry & Versioning MLesson 04: Feature Stores & Feature Engineering MLesson 05: CI/CD for ML Models MLesson 06: Model Deployment Strategies MLesson 07: A/B Testing & Experimentation MLesson 08: MLOps Best Practices & Governance

▸ Exercises

▸ Exercise 01 Mlops Maturity Assessment

▸ Exercise 02 Mlflow Tracking Deep Dive

▸ Exercise 03 Model Registry Promotion

▸ Exercise 04 Experiment Tracking Mlflow

▸ Exercise 05 Model Monitoring Drift

▸ Exercise 06 Ci Cd Ml Pipelines

▸ Exercise 07 Feature Store Implementation

▸ Exercise 08 Deployment Strategies

▸ Exercise 09 Ab Testing Infrastructure

▸ Exercise 10 Model Governance

▸ Exercise 11 Ml Orchestration Patterns

▸ Exercise 12 Model Cost Attribution

▸ Exercise 13 Reproducibility Audit

▸ Exercise 14 Ml Platform Operating Model

▸ Labs

MLab 01: MLflow Tracking Server End-to-End MLab 02: Model Registry and Stage Promotion MLab 03: Stand Up a Feast Feature Store MLab 04: CI/CD for Models with GitHub Actions MLab 05: Canary Deployment with Argo Rollouts MLab 06: A/B Test with Traffic Splitting MLab 07: Drift Monitoring with Evidently

▸ Quizzes

MModule 06: MLOps & Experiment Tracking - Quiz

MModule 06: MLOps & Experiment Tracking - Learning Resources

▸ Mod 107 Gpu Computing

MLesson 01: Introduction to GPU Computing for AI MLesson 02: CUDA Programming Fundamentals MLesson 03: PyTorch GPU Acceleration MLesson 04: Distributed Training Fundamentals MLesson 05: Multi-GPU Training Strategies MLesson 06: Model and Pipeline Parallelism MLesson 07: GPU Memory Management & Optimization MLesson 08: Advanced GPU Optimization

▸ Exercises

▸ Exercise 01 Gpu Introspection

▸ Exercise 02 Cuda Kernel

▸ Exercise 03 Pytorch Gpu Pipeline

▸ Exercise 04 Gpu Cluster Management

▸ Exercise 05 Gpu Performance Optimization

▸ Exercise 06 Distributed Gpu Training

▸ Exercise 07 Gpu Memory Profiling

▸ Exercise 08 Mixed Precision

▸ Exercise 09 Inference Optimization

▸ Exercise 10 Gpu Sharing Strategies

▸ Exercise 11 Gpu Cost Optimization

▸ Labs

MLab 01: CUDA Hello-World and Device Introspection MLab 02: PyTorch GPU Inference and Benchmarking MLab 03: Multi-GPU Training with DistributedDataParallel MLab 04: Run a GPU Pod on Kubernetes MLab 05: Mixed-Precision Training with torch.amp MLab 06: GPU Memory Profiling and OOM Diagnosis

▸ Quizzes

MModule 07: GPU Computing & Distributed Training - Quiz

MModule 07: GPU Computing & Distributed Training - Resources

▸ Mod 108 Monitoring Observability

MLesson 01: Introduction to Observability MLesson 02: Prometheus for Metrics Collection MLesson 03: Grafana for Visualization MLesson 04: Logging with ELK Stack and Loki MLesson 05: Distributed Tracing with Jaeger and Tempo MLesson 06: Alerting Strategies and Alert Management MLesson 07: ML-Specific Monitoring and Model Observability MLesson 08: Best Practices and Observability Culture

▸ Exercises

▸ Exercise 01 Observability Stack

▸ Exercise 02 Ml Model Monitoring

▸ Exercise 03 Promql Deep Dive

▸ Exercise 04 Dashboards As Code

▸ Exercise 05 Log Pipeline

▸ Exercise 06 Distributed Tracing Otel

▸ Exercise 07 Alertmanager Routing

▸ Exercise 08 Slo And Error Budgets

▸ Exercise 09 Incident Response Gameday

▸ Exercise 10 Observability Cost Control

▸ Labs

MLab 01: Instrument a Python Service with Prometheus MLab 02: Build a Grafana Dashboard from Scratch MLab 03: Send Logs to Loki MLab 04: Distributed Tracing with OpenTelemetry + Jaeger MLab 05: Author Alert Rules with Multi-Window Burn Rates

▸ Quizzes

MModule 08: Monitoring & Observability - Quiz

MModule 08: Monitoring & Observability - Resources

▸ Mod 109 Infrastructure As Code

MLesson 01: Introduction to Infrastructure as Code MLesson 02: Terraform Fundamentals MLesson 03: Terraform State Management MLesson 04: Building AI Infrastructure with Terraform MLesson 05: Pulumi - Infrastructure as Software MLesson 06: Advanced IaC Patterns MLesson 07: GitOps and IaC CI/CD MLesson 08: Security Best Practices for IaC

▸ Exercises

▸ Exercise 01 Terraform Ml Infrastructure

▸ Exercise 02 Pulumi Multicloud Ml

▸ Exercise 03 Terraform Module Library

▸ Exercise 04 State Management At Scale

▸ Exercise 05 Pulumi Advanced Patterns

▸ Exercise 06 Gitops Argocd

▸ Exercise 07 Secret Management

▸ Exercise 08 Policy As Code

▸ Exercise 09 Drift Detection

▸ Exercise 10 Multi Environment Promotion

▸ Exercise 11 Cost Aware Iac

▸ Exercise 12 Disaster Recovery Iac

▸ Exercise 13 Iac For Ml Workloads

▸ Labs

MLab 01: Terraform AWS VPC MLab 02: Terraform EKS Cluster MLab 03: Remote State Backend with S3 + DynamoDB Locking MLab 04: Pulumi Multi-Cloud Deployment MLab 05: IaC in CI/CD with PR-Based Plan MLab 06: Secret Management with Vault + Terraform MLab 07: Policy as Code with OPA Conftest

▸ Quizzes

MModule 09: Infrastructure as Code - Quiz

MModule 09: Infrastructure as Code - Resources

▸ Mod 110 Llm Infrastructure

MLesson 01: Introduction to LLM Infrastructure MLesson 02: vLLM Deployment MLesson 03: RAG Systems (Retrieval-Augmented Generation) MLesson 04: Vector Databases MLesson 05: LLM Fine-Tuning Infrastructure MLesson 06: LLM Serving Optimization MLesson 07: LLM Platform Architecture MLesson 08: Production LLM Best Practices

▸ Exercises

▸ Exercise 01 Production Llm Serving

▸ Exercise 02 Production Rag System

▸ Exercise 03 Vllm Deep Dive

▸ Exercise 04 Vector Db Benchmarking

▸ Exercise 05 Finetuning At Scale

▸ Exercise 06 Inference Optimization Llm

▸ Exercise 07 Multi Tenant Llm Platform

▸ Exercise 08 Llm Cost Routing

▸ Exercise 09 Llm Observability

▸ Exercise 10 Prompt Management

▸ Exercise 11 Llm Guardrails

▸ Exercise 12 Llm Evaluation Harness

▸ Exercise 13 Semantic Caching

▸ Exercise 14 Hybrid Retrieval Reranking

▸ Labs

MLab 01: Deploy an LLM with vLLM MLab 02: Build a RAG Pipeline End-to-End MLab 03: Stand Up a Vector Database (Qdrant) MLab 04: LoRA Fine-Tune a Small Model MLab 05: Streaming Responses to Clients MLab 06: Cost Tracking and Budget Alerts MLab 07: Prompt-Injection Defenses MLab 08: Multi-Model Serving with Intelligent Routing

▸ Quizzes

MModule 10: LLM Infrastructure - Quiz

MModule 10: LLM Infrastructure - Resources

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#Lessons

Each module directory should include a README.md, lecture notes, an exercises/ directory, a quiz, and module resources.