MLOps

MLOps Fundamentals

MLOps Foundations

Version Control for ML

ML Environments and Containers

Data and Features

Data Management

Feature Engineering and Stores

Model Artifacts Management

Training and Experiments

Tracking and Model Registry

Training Pipelines and Compute

Tuning and Reproducibility

Model Deployment

Model Serving and Inference

Scaling and Production Serving

Deployment Strategies

CI-CD and Testing

CI-CD and Pipeline Automation

Testing and Validation for ML

Cloud Infrastructure for ML

Monitoring and Operations

Monitoring and Drift Detection

Observability Infrastructure

Production Operations

Security and Governance

ML Security and Governance

Cost and Model Optimization

Project Best Practices