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Unified MLOps: Feature Stores & Model Deployment
The document discusses the challenges and solutions in production machine learning, emphasizing the significance of feature stores for model deployment and governance. It outlines key requirements for an effective feature store and presents Splice Machine's approach, highlighting scalability, speed, and compliance. The presentation also addresses the limitations of traditional pipelines and the need for a unified operational data platform.
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Unified MLOps: Feature Stores & Model Deployment
- 1. Unified MLOps: Feature Storesand Model Deployment Monte Zweben- CEO @ Splice Machine Jack Ploshnick- Data Scientist @ Splice Machine
- 2. Agenda ● Goals ofproduction machine learning ● Why are these goals hard to achieve? ● What is a Feature Store ● Feature Store Landscape ● Database Deployment & Feature Stores
- 3. Real-Time Machine LearningComponents Scale-Out Operational Data Platform Feature Store Re-usability, Governance, Serving Model Deployment Modeling Experimentation Scale-Out Analytical Data Platform
- 4. Real-Time Machine LearningComponents Scale-Out Operational Data Platform Feature Store Re-usability, Governance, Serving Model Deployment Modeling Experimentation Scale-Out Analytical Data Platform ML Landscape Today
- 5. Typical Machine LearningInfrastructure Bespoke pipelines Data Warehouse Database Real-Time Data Model 1 Dashboard Model 2
- 6. Pipeline Duplication isNot Enough Higher Compute Costs Recreating Features Lost Signal Data Lineage Nightmare
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- 8. What is aFeature Store? Real-Time Data Batch Data Feature Store Feature Search Training Sets Feature Serving Governance
- 9. Machine Learning witha Feature Store Feature Store Model 1 Data Warehouse Database Real-Time Data Dashboard Model 2
- 10. Feature Store Requirements ●Scales > 1B records ● Scales > 20K features ● Feature vector retrieval by primary key for inference <5ms-10ms ● Point-in-time consistency on training data ● Event-driven feature updates ● Batch feature updates ● Track feature lineage ● Discoverability and reuse with feature metadata ● Feature lineage ● Backfill of new features
- 11. Existing Architectures Raw Data Streaming (KVstore) Batch (Analytics Engine) Feature Store Consumer
- 12. Existing Architectures Raw Data Streaming (KVstore) Batch (Analytics Engine) Feature Store Consumer
- 13. Alternative Approach- HTAPDatabase Feature Serving
- 14. Challenges of HTAPDatabases ● In Memory ● Custom Hardware ● No support for secondary indexes or triggers ● Not ACID compliant
- 15. Splice Machine ● Scale-out ●Any Cloud/On-Prem ● Indexes and Triggers ● Full ACID Compliance
- 16. Feature Set Implementation FeatureSet Pipeline INSERT / UPDATE Initial Backfill
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- 19. Scalable & PersistentStorage of Predictions ● Easily track data drift ● Easily track concept drift ● Compare new models to history ● Fully audit-proof history
- 20. Database Deployment -Evaluation Store Prediction made and populated at millisecond speed
- 21. HTAP Database: FeatureStore + Deployment
- 22. Predictions Models FeaturesData Which model made that prediction? Which algorithm, parameters, and features were used to train the model? How were the features computed? What was the raw data at the time of training? Splice Machine Database Deployment Feature Store Guaranteed Lineage and Governance
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