Dmitry Spodarets: Modern MLOps toolchain 2023
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Dmitry Spodarets: Modern MLOps toolchain 2023 AI & BigData Online Day 2023 Spring Website - www.aiconf.com.ua Youtube - https://www.youtube.com/startuplviv FB - https://www.facebook.com/aiconf
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Dmitry Spodarets: Modern MLOps toolchain 2023
- 2. About Me Dmytro Spodarets ● DevOps Architect at Grid Dynamics ● Founder and chief editor of Data Phoenix AWS | Infrastructure | DevOps/MLOps
- 4. Agenda ● What is MLOps? ● DevOps vs MLOps ● MLOps Stack ● Use cases: ○ From research to production ○ Versioning & retraining ○ IaaC & K8s ○ Using GitOps for Machine Learning
- 6. CRISP-DM
- 7. Hidden Technical Debt in Machine Learning Systems https://papers.nips.cc/paper/2015/file/86df7dcfd896fcaf2674f757a2463eba-Paper.pdf
- 11. The goal of MLOps is to reduce technical friction to get the model from an idea into production in the shortest possible time with as little risk as possible.
- 12. DevOps vs MLOps
- 13. MLOps is not a single tool or platform
- 14. MLOps is about agreeing to do ML the right way and then supporting it.
- 15. A few shared principles will take you a long way… ML should be collaborative ML should be reproducible ML should be continuous ML should be tested & monitored
- 16. A few shared principles will take you a long way… ML should be collaborative ML should be reproducible ML should be continuous ML should be tested & monitored Shared Infrastructure Versioning for Code, Data and Metadata Machine Learning Pipelines Model Deployment and Monitoring
- 17. Continuous X MLOps is an ML engineering culture that includes the following practices: ● Continuous Integration (CI) extends the testing and validating code and components by adding testing and validating data and models. ● Continuous Delivery (CD) concerns with delivery of an ML training pipeline that automatically deploys another the ML model prediction service. ● Continuous Training (CT) is unique to ML systems property, which automatically retrains ML models for re-deployment. ● Continuous Monitoring (CM) concerns with monitoring production data and model performance metrics, which are bound to business metrics.
- 19. MLOps levels ● Level 0: No MLOps ● Level 1: DevOps no MLOps ● Level 2: Automated Training ● Level 3: Automated Model Deployment ● Level 4: Full MLOps Automated Retraining https://learn.microsoft.com/en-us/azure/architecture/example-scenario/mlops/mlops-maturity-model
- 20. MLOps Stack
- 23. AWS ML Stack
- 24. Use cases ○ From research to production ○ Versioning & retraining ○ IaaC & K8s ○ Using GitOps for Machine Learning
- 25. From research to production
- 26. From research to production
- 27. Data versioning & retraining
- 28. Data versioning & retraining
- 29. IaaC & K8s
- 30. Using GitOps for Machine Learning https://docs.dstack.ai/
- 32. Large language models / Generative models
- 34. Scalable HPC