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Databricks Overview for MLOps

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

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Databricks Overview for MLOps Clemens Mewald Director of Product Management
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
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
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
(Git-based) Projects Version Review Test Development / Experimentatio n Production Jobs Git / CI/CD Systems CI/CD Integration ▲ ▼ Supported Git Providers
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
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
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
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
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
Distributed Tuning ▪ Built-in support in the ML Runtime Worker Nodes Driver Trials Integration
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
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
Support for all Deployment Modes Deploying an MLLib model as a Spark UDF
Support for all Deployment Modes Deploying an MLLib model as a Spark UDF Deploying a Scikit Learn model as a Spark UDF
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
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.
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
Powered by Data Governance Experiment Tracking Reproducibility Model Governance Data Source / Lineage Data Versioning Automated Data Source capture and Versioning
Powered by Data Governance Experiment Tracking Reproducibility Model Governance Feature-Level Data Lineage / Usage Automated capture of Feature Usage
Powered by Data Governance Experiment Tracking Reproducibility Model Governance Parameters Metrics Models Artifacts Automated capture of ML metrics, parameters, artifacts, etc.
Powered by Data Governance Experiment Tracking Reproducibility Model Governance Trials Automated capture of Hyperparameter Search
Powered by Data Governance Experiment Tracking Reproducibility Model Governance Model Interpretability Automated Model Interpretability
Powered by Data Governance Experiment Tracking Reproducibility Model Governance Code Versioning Cluster Configuration Environment Configuration Automated capture of Code, Environment and Cluster Specification
Powered by Data Governance Experiment Tracking Reproducibility Model Governance Model Discoverability Model Stage-Based ACLs Model Sharing, Reuse, and ACLs
Powered by Data Governance Experiment Tracking Reproducibility Model Governance Approval Process for Stage Transitions Audit Log of Model Changes Automated Model Lineage and Governance
Powered by Data Governance Experiment Tracking Reproducibility Model Governance Turnkey Serving integrated with Model Versions and Stages Turnkey Model Serving
Data Governance Experiment Tracking Reproducibility Model Governance Quality / Performance Metric Monitoring Powered by Model Quality monitoring
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
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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Databricks Overview for MLOps

  • 1. Databricks Overview for MLOps Clemens Mewald Director of Product Management
  • 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. 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. 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. (Git-based) Projects Version Review Test Development / Experimentatio n Production Jobs Git / CI/CD Systems CI/CD Integration ▲ ▼ Supported Git Providers
  • 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. 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. 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. 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. 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. Distributed Tuning ▪ Built-in support in the ML Runtime Worker Nodes Driver Trials Integration
  • 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. 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. Support for all Deployment Modes Deploying an MLLib model as a Spark UDF
  • 15. Support for all Deployment Modes Deploying an MLLib model as a Spark UDF Deploying a Scikit Learn model as a Spark UDF
  • 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. 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. 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. Powered by Data Governance Experiment Tracking Reproducibility Model Governance Data Source / Lineage Data Versioning Automated Data Source capture and Versioning
  • 20. Powered by Data Governance Experiment Tracking Reproducibility Model Governance Feature-Level Data Lineage / Usage Automated capture of Feature Usage
  • 21. Powered by Data Governance Experiment Tracking Reproducibility Model Governance Parameters Metrics Models Artifacts Automated capture of ML metrics, parameters, artifacts, etc.
  • 22. Powered by Data Governance Experiment Tracking Reproducibility Model Governance Trials Automated capture of Hyperparameter Search
  • 23. Powered by Data Governance Experiment Tracking Reproducibility Model Governance Model Interpretability Automated Model Interpretability
  • 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. Powered by Data Governance Experiment Tracking Reproducibility Model Governance Model Discoverability Model Stage-Based ACLs Model Sharing, Reuse, and ACLs
  • 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. Powered by Data Governance Experiment Tracking Reproducibility Model Governance Turnkey Serving integrated with Model Versions and Stages Turnkey Model Serving
  • 28. Data Governance Experiment Tracking Reproducibility Model Governance Quality / Performance Metric Monitoring Powered by Model Quality monitoring
  • 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. 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