Day 1: Introduction to MLflow and Experiment Tracking

• Introduction to MLOps and its importance
• Overview of MLflow and its core components: Tracking, Projects, Models, Registry
• Setting up MLflow Tracking: Local and remote servers
• Logging parameters, metrics, and artifacts
• Visualizing experiments and comparing runs
• Advanced experiment tracking techniques:Conditional loggingHierarchical runsAutomatic logging with MLflow autolog
• Best practices for organizing and managing experiments

Day 2: Model Registry and Model Deployment

• Introduction to Model Registry
• Registering models, model versions, and model stages
• Model versioning and promotion
• Model search and filtering
• Model deployment with MLflow Models:Serving models as REST APIsPackaging models for deployment
• Model deployment with MLflow Model Registry:Deploying models to various platforms (e.g., local, cloud, Kubernetes)Model monitoring and retrainingModel lineage tracking

Day 3: MLflow Projects and Reproducibility

• Introduction to MLflow Projects
• Creating reproducible ML projects with MLflow Projects
• Packaging and sharing ML projects
• Project execution and parameterization
• Best practices for project organization and version control
• Advanced project techniques:Conditional executionParameter sweepsAutomated hyperparameter tuning with MLflow
• Integrating MLflow Projects with CI/CD pipelines

Day 4: Real-World MLflow Use Cases and Best Practices

• Real-world MLflow use cases:MLOps in productionCollaborative ML workflowsData science team collaboration
• Best practices for effective MLflow usage:Organizing experimentsManaging model versionsDeploying models reliably
• Hands-on workshop:Building a complete ML pipeline with MLflowDeploying a model to a production environmentMonitoring and retraining modelsTroubleshooting common MLflow issues