MLOps Virtual Event: Building ML Platforms
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This document summarizes a webinar on building machine learning platforms. It discusses how operating ML models is complex, requiring tasks like monitoring performance, handling data drift, and ensuring governance and security. It then outlines common components of ML platforms, including data management, model management, and code/deployment management. The webinar will demonstrate how different organizations handle these components and include demos from four companies. It will also cover Databricks' approach to providing an ML platform that integrates various tools and simplifies the full ML lifecycle from data preparation to deployment.
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MLOps Virtual Event: Building ML Platforms
- 1. MLOps Virtual Event: Building Machine Learning Platforms Matei Zaharia Chief Technologist, Databricks @matei_zaharia
- 3. Even After Deploying, Operating ML is Complex! Monitoring performance of the model Data drift Governance and security Many ML teams spend >50% of their time maintaining existing models
- 4. Why is ML Hard to Operationalize? Dependence on data Multiple, application-specific ways to evaluate performance Many teams and systems involved Data Prep Training Deployment Raw Data ML ENGINEER APPLICATIO N DEVELOPER DATA ENGINEE R
- 5. Response: ML Platforms Software platforms to manage ML applications, from development to production Most companies that use ML at scale are building one Tech examples: Facebook FBLearner, Google TFX, Uber Michelangelo
- 6. Common Components in an ML Platform Data management, in development and at scoring time ▪ Data transformation, quality monitoring, data versioning ▪ Feature stores Model management ▪ Packaging, review, quality assurance, versioning Code and deployment management ▪ Reproducibility, deployment, monitoring, experimentation ModelDB
- 7. Our Approach at Databricks Every team’s requirements will be different, and will change over time Provide a general platform that is easy to integrate with diverse tools Open source machine learning platform Transactional, versioned data lake storage Data science & ML workspace
- 8. In This Webinar How we and other organizations handle the different components of a machine learning platform Demos and experience from 4 different companies
- 9. End-to-End Data Science and Machine Learning on Databricks Clemens Mewald Director of Product Management, Databricks
- 10. End-to-End Data Science and ML on AutoML End-to-End ML Lifecycle ML Runtime and Environments Batch Scoring Online Serving Data Science Workspace Prep Data Build Model Deploy/Monitor Model Open,pluggable architecture
- 11. End-to-End Data Science and ML on AutoML End-to-End ML Lifecycle Batch Scoring Online Serving Data Science Workspace Prep Data Build Model Deploy/Monitor Model Open,pluggable architecture ML Runtime and Environments
- 12. Projects Packaging format for reproducible runs on any compute platform Components
- 13. Models General model format that standardizes deployment options Projects Packaging format for reproducible runs on any compute platform Components
- 14. Tracking Record and query experiments: code, metrics, parameters, artifacts, models Models General model format that standardizes deployment options Projects Packaging format for reproducible runs on any compute platform Components
- 15. Tracking Record and query experiments: code, metrics, parameters, artifacts, models Models General model format that standardizes deployment options Model Registry Centralized and collaborative model lifecycle management Projects Packaging format for reproducible runs on any compute platform Components
- 17. Model Lifecycle Models Tracking Flavor 2 Flavor 1 Custom Models Parameter s Metrics Artifacts ModelsMetadata
- 18. Model Lifecycle Staging Production Archived Data Scientists Deployment Engineers v1 v2 v3 Models Tracking Flavor 2 Flavor 1 Model Registry Custom Models Parameter s Metrics Artifacts ModelsMetadata
- 19. Model Lifecycle Staging Production Archived Data Scientists Deployment Engineers v1 v2 v3 Models Tracking Flavor 2 Flavor 1 Model Registry Custom Models In-Line Code Containers Batch & Stream Scoring Cloud Inference Services OSS Serving Solutions Serving Parameter s Metrics Artifacts ModelsMetadata
- 20. Parameters and (a time series of) metrics Artifacts (including model) Auto-logging for ML Frameworks: A single line of code logs parameters, metrics, and artifacts. mlflow.keras.autolog() # or: mlflow.tensorflow.autolog() Auto-Logging
- 21. End-to-End Data Science and ML on AutoML End-to-End ML Lifecycle Batch Scoring Online Serving Data Science Workspace Prep Data Build Model Deploy/Monitor Model Open,pluggable architecture ML Runtime and Environments
- 22. Enterprise Ready Enterprise grade access controls, identity pass-through, and auditability Collaborative Realtime co-editing and commenting Reproducible Auto-logged revision history and Git integration for version control Visualizations Built-in visualizations and support for the most popular visualization libraries (e.g. matplotlib, ggplot) Experiment Tracking Built-in tracking of Data Science and ML experiments, with metrics, parameters, artifacts, and more Multi-Language Scala, SQL, Python, R: All in one notebook Databricks Notebooks Provide a collaborative environment for Unified Data Analytics
- 23. Databricks Notebooks for Collaborative Data Science Data Engineers, Data Scientists, ML Engineers, and Data Analysts can all collaborate in one shared environment using modern collaboration patterns. Co-Presence / Co-Editing CommentingVersioning
- 24. Integration with Databricks Notebooks ● Runs Sidebar integrated with MLflow Tracking ● Track runs, sort by metrics and parameters ● Linked to revision history of the notebook
- 25. End-to-End Data Science and ML on AutoML End-to-End ML Lifecycle Batch Scoring Online Serving Data Science Workspace Prep Data Build Model Deploy/Monitor Model Open,pluggable architecture ML Runtime and Environments
- 26. Your Existing Data Lake Ingestion Tables Data Catalog Feature Store Azure Data Lake Storage Amazon S3 Streaming Batch 3rd Party Data Marketplace Files for Data Science and ML ● Schema enforced high quality data ● Optimized performance ● Full data lineage / governance ● Reproducibility through time travel ML Runtime
- 27. for Data Science and ML Ingest data and visualize data distribution
- 28. for Data Science and ML Data versioning and time travel
- 29. for Data Science and ML Data versioning and time travel
- 30. Integration with Delta Auto-Logging for any Spark Datasource
- 31. End-to-End Data Science and ML on AutoML End-to-End ML Lifecycle Batch Scoring Online Serving Data Science Workspace Prep Data Build Model Deploy/Monitor Model Open,pluggable architecture ML Runtime and Environments
- 32. Packages and optimizes most common ML Frameworks ... Machine Learning Runtime
- 33. Packages and optimizes most common ML Frameworks ... Built-in Optimization for Distributed Deep Learning Distribute and Scale any Single-Machine ML Code to 1,000’s of machines Machine Learning Runtime
- 34. Built-In AutoML and Experiment Tracking Packages and optimizes most common ML Frameworks ... Built-in Optimization for Distributed Deep Learning Distribute and Scale any Single-Machine ML Code to 1,000’s of machines AutoML and Tracking / Visualizations with MLflow Machine Learning Runtime
- 35. Machine Learning Pre-configured Environment Customizatio n requirements.txt Built-In AutoML and Experiment Tracking conda.yaml Packages and optimizes most common ML Frameworks ... Built-in Optimization for Distributed Deep Learning Distribute and Scale any Single-Machine ML Code to 1,000’s of machines Customized Environments using Conda Conda- BasedAutoML and Tracking / Visualizations with MLflow Machine Learning Runtime
- 36. Integration with ML Runtime Hyperopt autlogging to MLflow
- 37. End-to-End Data Science and ML on AutoML End-to-End ML Lifecycle Batch Scoring Online Serving Data Science Workspace Prep Data Build Model Deploy/Monitor Model Open,pluggable architecture ML Runtime and Environments
- 38. Model Deployment Staging Production Archived Data Scientists Deployment Engineers v1 v2 v3 Model Registry In-Line Code Containers Batch & Stream Scoring Cloud Inference Services OSS Serving Solutions Serving
- 39. Model Deployment model_udf = mlflow.pyfunc.spark_udf( spark, model_uri='models:/forecast/production') Staging Production Archived Data Scientists Deployment Engineers v1 v2 v3 Model Registry In-Line Code Containers Batch & Stream Scoring Cloud Inference Services OSS Serving Solutions Serving
- 40. In summary, Databri cks accelerates the full ML Lifecycle AutoML End-to-End ML Lifecycle Batch Scoring Online Serving Data Science Workspace Prep Data Build Model Deploy/Monitor Model Open,pluggable architecture ML Runtime and Environments