Building a Scalable and reliable open source ML Platform with MLFlow
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This document discusses building a scalable and open source machine learning platform. It introduces MLOps and describes ING's ML batch platform use case. The machine learning lifecycle is presented, noting that operationalizing machine learning models is difficult due to infrastructure deployment challenges, lack of collaboration and standardization. An ideal MLOps approach is described with flexible, scalable, automated and standardized processes. Benefits of ING's MLOps approach include increased efficiency, speed, quality, security and auditability. Open source tools that could be leveraged are also presented.
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Building a Scalable and reliable open source ML Platform with MLFlow
- 1. Cristiano Rocha and Bart Buter Building a scalable & open source ML platform
- 2. • Introduction to MLOps • Use case: ING ML batch platform • Exploration to production • Considerations Agenda
- 3. Sander van Donkelaar Machine Learning Engineer Xebia Data About us… Cristiano Rocha Principal AI & Data engineer Xebia Data Bart Buter Batch Execution Platform P.O. & Analytics Platform Adoption Lead ING
- 4. The machine learning lifecycle
- 5. The machine learning lifecycle
- 6. The machine learning lifecycle
- 7. The machine learning lifecycle
- 8. But machine learning in production is hard…
- 9. • Deploying a model requires significant efforts in the IT infrastructure. • Difficult for other applications to leverage produced insights. • Data scientist != MLOps Engineer • Deploying a model requires a different skillset than building it. • No CI/CD • Lack of collaboration • Lack of best-practices • No standardization Productionalizing ML: market observations People Technology Processes
- 10. • Flexible • Scalable • Innovative • Cross-functional • Self-reliant • Collaboration • Standardized • Automated • Self-service • Repeatable MLOps: the ideal world People Technology Processes
- 11. MLOps: business value for ING Auditability • Increased efficiency & reusability. • Reduced time to value • Rapid onboarding of new use-cases. • Automation Speed Quality • Incorporate software engineering best practices • Consistency • Reliability • Transparancy • Model- and data lineage • Reproducibility Security
- 12. Teams should be able to deploy train- and deploy their own models, without relying on support from ops teams Solution should fully leverage open-source and cloud native tooling (Kubernetes, Mlflow, Airflow etc.) It should be possible to dynamically distribute- and scale workloads to distribute resources efficiently ING: some extra requirements for MLOps Open-Source / (Private) Cloud Self-service Scalable Solution should service multiple customers. Permissions should be determined on the basis of roles- and group memberships. Multi-tenancy
- 13. ING Analytics Platforms Exploration Execution Private cloud Public cloud API Streaming Exploration & Model Development Batch Public Cloud Exploration Public Cloud Execution
- 14. Platform Maturity Time Sandbox environment Standardize deployment and onboarding processeses Implement quality checks, introduce data- and model lineage. Introduce automatic retraining. Self-service ML Standardize- and centralize monitoring of ML models. Automated ML solution onboarding Experiment Consistent Reliable Scalable & Optimized MLOps maturity levels
- 16. Open source software Con’s • (Usually) not enterprise-ready as it requires modifications or extensions to meet organazational’s requirements • Higher overhead (e.g., deployment on more complex scenarios) • Lack of vendor support for criticas issues (e.g. SLA’s) Pro’s • Modular: solves a specific purpose • Community: embedded best practices and more support for questions and issues • Flexibility through customization • Quicker updates and vulnerability fixes • Reduced vendor lock-in risks • Lower vendor costs • Reduces integration costs
- 17. Open source tools & frameworks 1/2 Kubernete s System for orchestrating and automating deployment, scaling and management of containerized applications. MLFlo w Platform for managing end-to-end machine learning lifecycle (including experiment tracking, model management, deployment and registry). Airflo w Platform for creating, scheduling and monitoring workflow. When such workflows are defined as code, they become more maintainable, versionable, testable, and collaborative.
- 18. Open source tools & frameworks 2/2 Spar k Engine for executing data engineering, data science and machine learning workloads in a distributed manner for fast analytic queries against data of any size. NGIN X HTTP and reverse proxy server. As a reverse proxy, it sits in front of servers and forward client request to such servers. Typically, it is added to help increasing security and reliability. Open Policy Agent General-purpose engine to enforce policies in a decoupled manner. OPA provides a declarative language that lets you specify policy as code and simple APIs to offload policy decision-making from an application logic.
- 22. Role-based access control on MLFlow OSS Open Policy Agent policies description (policies.rego)
- 23. Golden path template Template for training a model
- 24. Template for promoting models to Production
- 25. Template for promoting models to Production
- 26. Automatically set model metadata
- 27. • Use Golden-path repositories to create examples that incorporate best practices. • Solution must be flexible enough to cover a wide variety of use cases. • But this should not come at the cost of complexity! • Modular: More difficult, requires a wide variety of domain knowledge. • End2End: Easier, but it can give problems in the long term. Considerations Flexibility vs. Complexity Self-Service vs. Best-Practices Modular vs. End2End