Ad
More Related Content
What's hot (20)
Similar to The A-Z of Data: Introduction to MLOps (20)
![[DSC Europe 22] Why you need to think about MLOps at the beginning of your pr...](https://cdn.slidesharecdn.com/ss_thumbnails/dmitriifursov-whyyouneedtothinkaboutmlops-221130084921-1605c862-thumbnail.jpg?width=560&fit=bounds)
![[DSC Adria 23] Mikhail Rozhkov DVC in Machine Learning Engineering and MLOps ...](https://cdn.slidesharecdn.com/ss_thumbnails/05mikhailrozhkovdvcinmachinelearningengineeringandmlopspracticesv1-230530184512-e1c8bceb-thumbnail.jpg?width=560&fit=bounds)
Ad
More from DataPhoenix (7)
Ad
Recently uploaded (20)
The A-Z of Data: Introduction to MLOps
- 3. The A-Z of Data MLOps, Natural Language Processing, Computer Vision, Time-Series Forecasting 17 August – Introduction to MLOps 25 August – Monitoring Machine Learning Models in Production 31 August – From research to product with Hydrosphere 8 September – Kubeflow DVC / use case webinar and expert panel discussion
- 4. The A-Z of Data: Introduction to MLOps
- 5. About Me Dmitry Spodarets ● Head of R&D & ML competency at VITech ● Founder and chief editor of Data Phoenix ● Active participant of the ODS.ai community
- 7. Agenda ● What is MLOps? ● Principles and Practices ● ML processes and tools
- 9. CRISP-DM
- 10. Core phases for ML solution Experimental phase QA phase Prod phase
- 11. Hidden Technical Debt in Machine Learning Systems https://papers.nips.cc/paper/2015/file/86df7dcfd896fcaf2674f757a2463eba-Paper.pdf
- 15. 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.
- 16. MLOps is not a single tool or platform
- 17. MLOps is about agreeing to do ML the right way and then supporting it.
- 18. A few shared principles will take you a long way… ML should be collaborative
- 19. A few shared principles will take you a long way… ML should be reproducible
- 20. A few shared principles will take you a long way… ML should be continuous
- 21. A few shared principles will take you a long way… ML should be tested & monitored
- 22. 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.
- 23. And tooling will help implement your process ML should be collaborative Shared Infrastructure
- 24. And tooling will help implement your process ML should be reproducible Versioning for Code, Data and Metadata
- 25. And tooling will help implement your process ML should be continuous Machine Learning Pipelines
- 26. And tooling will help implement your process ML should be tested & monitored Model Deployment and Monitoring
- 28. Machine Learning Process Research Build & Train Model Deploy Model Prepare data Predictions
- 29. Machine Learning Process Research & Discovery Data storage Data validation Data extraction & collection Data labeling Model validation Model training Feature engineering / feature storage Model evaluation Data preparation Model storage Model serving Model optimization Monitoring Predictions
- 30. MLOps levels ● Level 0 - Manual process. ● Level 1 - ML pipeline automation. ● Level 2 - CI/CD pipeline automation.
- 31. MLOps level 0: Manual process https://cloud.google.com/architecture/mlops-continuous-delivery-and-automation-pipelines-in-machine-learning
- 32. MLOps level 1: ML pipeline automation https://cloud.google.com/architecture/mlops-continuous-delivery-and-automation-pipelines-in-machine-learning
- 33. MLOps level 2: CI/CD pipeline automation https://cloud.google.com/architecture/mlops-continuous-delivery-and-automation-pipelines-in-machine-learning
- 34. MLOps Stack ● LF AI & Data landscape - https://landscape.lfai.foundation/ ● THE 2020 DATA & AI LANDSCAPE - https://mattturck.com/data2020/
- 35. MLOps Stack ● All-in-one tools ● CI/CD ● Data & Model registry, Tracking Experiments. ● Model serving ● Model monitoring
- 36. Jupyter Notebooks Notebooks have become fundamental to data science. Problems with notebooks: ● Hard to version ● Very hard to test ● Out-of-order execution artifacts Hard to run long or distributed tasks
- 37. Netflix bases all ML workflows on Jupyter Notebooks https://netflixtechblog.com/notebook-innovation-591ee3221233
- 38. MLOps Stack: All-in-one tools
- 39. MLOps Stack: All-in-one tools
- 40. MLOps Stack: All-in-one tools
- 42. MLOps Stack: Data&Model registry, tracking experiments DIY: Store in S3 or push to Git or …
- 43. MLOps Stack: additional dev stack ● Framework - Catalyst (PyTorch) ● Augmentations - Albumentations ● Code style - black+isort ● Type descriptions - mypy ● Testing - pytest+hypothesis
- 44. MLOps Stack: Model serving DIY: Implement model server (Flask App), Dockerize, Expose API (HTTP, gRPC, batch, Streaming)... Frameworks & tools: TensorFlow Serving, TorchServe, NVIDIA Triton Inference Server...
- 46. MLOps Stack: Model serving DIY: Implement model server (Flask App), Dockerize, Expose API (HTTP, gRPC, batch, Streaming)... Frameworks & tools: TensorFlow Serving, TorchServe, NVIDIA Triton Inference Server...
- 47. NVIDIA Triton Inference Server
- 48. Amazon SageMaker Edge Manager
- 49. MLOps Stack: Model monitoring DIY: Amazon CloudWatch
- 50. Example of ML solution architecture