1. Data Handling and Versioning

• Execute read and write operations on Delta tables
• Access historical versions of Delta tables and perform version restoration
• Create, update, and merge Feature Store tables for machine learning workflows

2. Experimental Logging and Tracking

• Log parameters, models, and evaluation metrics manually using MLflow
• Programmatically retrieve and utilize data, metadata, and models from MLflow experiments

3. Advanced Experimentation Techniques

• Conduct MLflow experiment tracking with model signatures and input examples
• Configure and utilize nested runs and enable autologging with Hyperopt
• Record and analyze artifacts including SHAP plots, custom visualizations, feature data, images, and metadata

1. Preprocessing and Flavor Management

• Understand MLflow flavors and their benefits, including the pyfunc flavor
• Integrate preprocessing logic and contextual information within custom model classes and objects

2. Model Registration and Administration

• Navigate Model Registry functionalities, including model registration, metadata management, and stage transitions
• Register new models and versions programmatically and manage model lifecycle stages (e.g., transition, archive, delete)

3. Lifecycle Automation and CI/CD Integration

Automate model lifecycle processes using Model Registry Webhooks and Databricks Jobs

Create Jobs triggered by model stage changes and integrate Webhooks for automated workflows

Manage HTTP webhooks, including creation, listing, and deletion

1. Delta Lake Fundamentals

• Explain Delta Lake’s transaction log and cloud object storage for ensuring atomicity and durability
• Describe Delta Lake’s Optimistic Concurrency Control for transaction isolation and conflict resolution
• Detail the functionality of Delta Lake’s cloning features

2. Optimization and Indexing

• Apply Delta Lake indexing techniques, including partitioning, Z-order indexing, bloom filters, and file size management
• Optimize Delta tables for performance in Databricks SQL service

1. Drift Analysis

• Differentiate between label drift, feature drift, and concept drift
• Assess scenarios likely to exhibit feature drift and label drift and evaluate their impact on model performance

2. Drift Monitoring and Testing

• Utilize summary statistics and robust statistical tests for monitoring numeric and categorical feature drift
• Apply Jenson-Shannon divergence, Kolmogorov-Smirnov, and chi-square tests for drift detection and analysis

3.Comprehensive Drift Management

• Develop workflows for measuring and addressing concept drift and feature drift
• Implement model retraining and assess model performance on updated data to address detected drift