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Databricks Overview for MLOps

Databricks Overview for MLOps by Clemend Mewald, Director of Product Management, Databricks

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Databricks Overview for MLOps

  1. 1. Databricks Overview for MLOps Clemens Mewald Director of Product Management
  2. 2. MLOps / Governance The Databricks ML Platform Data Science Workspace Data Ingestion Data Versioning Model Training Model Tuning Runtime and Environments Monitoring Batch Scoring Online Serving
  3. 3. DATA ENGINEERS DATA SCIENTISTS ML ENGINEERS DATA ANALYSTS Collaborative Data Science Workspace MLOps / Governance Data Science Workspace Data Ingestion Data Versioning Model Training Model Tuning Runtime and Environments Monitoring Batch Scoring Online Serving
  4. 4. Data Science Workspace DATA ENGINEERS DATA SCIENTISTS Cloud-native Collaboration Features Commenting Co-Presence Co-Editing Multi-Language Scala, SQL, Python, R: All in one notebook. Collaborative Realtime co-presence, co-editing, and commenting. Databricks Notebooks ML ENGINEERS DATA ANALYSTS
  5. 5. (Git-based) Projects Version Review Test Development / Experimentatio n Production Jobs Git / CI/CD Systems CI/CD Integration ▲ ▼ Supported Git Providers
  6. 6. MLOps / Governance High Quality Data at Scale Data Science Workspace Data Ingestion Data Versioning Model Training Model Tuning Runtime and Environments Monitoring Batch Scoring Online Serving
  7. 7. High Quality Data at Scale Structured, Semi-Structured and Unstructured Data Business Intelligence Data Science Machine Learning Delta Lake Data Science Workspace MLflow Workspace SQL Analytics Ingest any format at any scale from any source ACID transactions guarantee data validity Versioning and time-travel built-in Automated logging of data + version information
  8. 8. Turnkey ML Training at Scale MLOps / Governance Data Science Workspace Data Ingestion Data Versioning Model Training Model Tuning Runtime and Environments Monitoring Batch Scoring Online Serving
  9. 9. ML Runtime: DevOps-free Environment optimized for Machine Learning Packages up the most popular ML Toolkits Simplifies Distributed ML/DL Distribute and scale any single-machine ML code to 1,000’s of machines. Built-in AutoML and Auto-Logging Hyperparameter tuning, AutoML, automated tracking, and visualizations with MLflow Turnkey ML Training at Scale
  10. 10. Distributed Training ▪ Built-in support in the ML Runtime TensorFlow native Distribution Strategy (Spark TensorFlow Distributor) HorovodRunner (Keras, TensorFlow, and PyTorch) Worker Nodes Driver Training Tasks
  11. 11. Distributed Tuning ▪ Built-in support in the ML Runtime Worker Nodes Driver Trials Integration
  12. 12. Support for all Deployment Modes MLOps / Governance Data Science Workspace Data Ingestion Data Versioning Model Training Model Tuning Runtime and Environments Monitoring Batch Scoring Online Serving
  13. 13. Models Tracking Flavor 2 Flavor 1 Custom Models In-Line Code Containers Batch & Stream Scoring Cloud Inference Services OSS Serving Solutions Parameters Metrics Artifacts Models Metadata Deployment Options Staging Production Archived Data Scientists Deployment Engineers v2 v3 v1 Model Registry Support for all Deployment Modes
  14. 14. Support for all Deployment Modes Deploying an MLLib model as a Spark UDF
  15. 15. Support for all Deployment Modes Deploying an MLLib model as a Spark UDF Deploying a Scikit Learn model as a Spark UDF
  16. 16. Support for all Deployment Modes Deploying an MLLib model as a Spark UDF Deploying a Scikit Learn model as a Spark UDF Deploying a TensorFlow model as a Spark UDF
  17. 17. Support for all Deployment Modes Deploying an MLLib model as a Spark UDF Deploying a Scikit Learn model as a Spark UDF Deploying a TensorFlow model as a Spark UDF Yes, they’re all the same! As are the commands to deploy these models as Docker containers, etc.
  18. 18. Data Science Workspace Data Ingestion Data Versioning Model Training Model Tuning Runtime and Environments Monitoring Batch Scoring Online Serving Data Governance Powered by Experiment Tracking Reproducibility Model Governance End-to-end MLOps / Governance
  19. 19. Powered by Data Governance Experiment Tracking Reproducibility Model Governance Data Source / Lineage Data Versioning Automated Data Source capture and Versioning
  20. 20. Powered by Data Governance Experiment Tracking Reproducibility Model Governance Feature-Level Data Lineage / Usage Automated capture of Feature Usage
  21. 21. Powered by Data Governance Experiment Tracking Reproducibility Model Governance Parameters Metrics Models Artifacts Automated capture of ML metrics, parameters, artifacts, etc.
  22. 22. Powered by Data Governance Experiment Tracking Reproducibility Model Governance Trials Automated capture of Hyperparameter Search
  23. 23. Powered by Data Governance Experiment Tracking Reproducibility Model Governance Model Interpretability Automated Model Interpretability
  24. 24. Powered by Data Governance Experiment Tracking Reproducibility Model Governance Code Versioning Cluster Configuration Environment Configuration Automated capture of Code, Environment and Cluster Specification
  25. 25. Powered by Data Governance Experiment Tracking Reproducibility Model Governance Model Discoverability Model Stage-Based ACLs Model Sharing, Reuse, and ACLs
  26. 26. Powered by Data Governance Experiment Tracking Reproducibility Model Governance Approval Process for Stage Transitions Audit Log of Model Changes Automated Model Lineage and Governance
  27. 27. Powered by Data Governance Experiment Tracking Reproducibility Model Governance Turnkey Serving integrated with Model Versions and Stages Turnkey Model Serving
  28. 28. Data Governance Experiment Tracking Reproducibility Model Governance Quality / Performance Metric Monitoring Powered by Model Quality monitoring
  29. 29. Code versioning Data versioning Cluster configuration Environment specification Auto-Logging Reproducibility Checklist Reproduce Run Feature Data Governance Experiment Tracking Reproducibility Model Governance Powered by ✓ ✓ ✓ ✓ The Result: Full End-to-End Governance and Reproducibility
  30. 30. MLOps / Governance The Databricks ML Platform Data Science Workspace Data Ingestion Data Versioning Model Training Model Tuning Runtime and Environments Monitoring Batch Scoring Online Serving

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