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The A-Z of Data: Introduction to MLOps

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DataPhoenix

Команда Data Phoenix Events приглашает всех, 17 августа в 19:00, на первый вебинар из серии "The A-Z of Data", который будет посвящен MLOps. В рамках вводного вебинара, мы рассмотрим, что такое MLOps, основные принципы и практики, лучшие инструменты и возможные архитектуры. Мы начнем с простого жизненного цикла разработки ML решений и закончим сложным, максимально автоматизированным, циклом, который нам позволяет реализовать MLOps. https://dataphoenix.info/the-a-z-of-data/ https://dataphoenix.info/the-a-z-of-data-introduction-to-mlops/

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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
The A-Z of Data: Introduction to MLOps
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
Agenda ● What is MLOps? ● Principles and Practices ● ML processes and tools
CRISP-DM
Core phases for ML solution Experimental phase QA phase Prod phase
Hidden Technical Debt in Machine Learning Systems https://papers.nips.cc/paper/2015/file/86df7dcfd896fcaf2674f757a2463eba-Paper.pdf
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.
MLOps is not a single tool or platform
MLOps is about agreeing to do ML the right way and then supporting it.
A few shared principles will take you a long way… ML should be collaborative
A few shared principles will take you a long way… ML should be reproducible
A few shared principles will take you a long way… ML should be continuous
A few shared principles will take you a long way… ML should be tested & monitored
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.
And tooling will help implement your process ML should be collaborative Shared Infrastructure
And tooling will help implement your process ML should be reproducible Versioning for Code, Data and Metadata
And tooling will help implement your process ML should be continuous Machine Learning Pipelines
And tooling will help implement your process ML should be tested & monitored Model Deployment and Monitoring
Machine Learning Process Research Build & Train Model Deploy Model Prepare data Predictions
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
MLOps levels ● Level 0 - Manual process. ● Level 1 - ML pipeline automation. ● Level 2 - CI/CD pipeline automation.
MLOps level 0: Manual process https://cloud.google.com/architecture/mlops-continuous-delivery-and-automation-pipelines-in-machine-learning
MLOps level 1: ML pipeline automation https://cloud.google.com/architecture/mlops-continuous-delivery-and-automation-pipelines-in-machine-learning
MLOps level 2: CI/CD pipeline automation https://cloud.google.com/architecture/mlops-continuous-delivery-and-automation-pipelines-in-machine-learning
MLOps Stack ● LF AI & Data landscape - https://landscape.lfai.foundation/ ● THE 2020 DATA & AI LANDSCAPE - https://mattturck.com/data2020/
MLOps Stack ● All-in-one tools ● CI/CD ● Data & Model registry, Tracking Experiments. ● Model serving ● Model monitoring
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
Netflix bases all ML workflows on Jupyter Notebooks https://netflixtechblog.com/notebook-innovation-591ee3221233
MLOps Stack: All-in-one tools
MLOps Stack: All-in-one tools
MLOps Stack: All-in-one tools
MLOps Stack: CI/CD
MLOps Stack: Data&Model registry, tracking experiments DIY: Store in S3 or push to Git or …
MLOps Stack: additional dev stack ● Framework - Catalyst (PyTorch) ● Augmentations - Albumentations ● Code style - black+isort ● Type descriptions - mypy ● Testing - pytest+hypothesis
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...
Model Serving Patterns https://ml-ops.org/
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...
NVIDIA Triton Inference Server
Amazon SageMaker Edge Manager
MLOps Stack: Model monitoring DIY: Amazon CloudWatch
Example of ML solution architecture
Questions? Dmitry Spodarets d.spodarets@dataphoenix.info https://dataphoenix.info https://vitechteam.com

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The A-Z of Data: Introduction to MLOps

  • 1.
  • 2.
  • 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
  • 6.
  • 7. Agenda ● What is MLOps? ● Principles and Practices ● ML processes and tools
  • 8.
  • 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
  • 12.
  • 13.
  • 14.
  • 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
  • 27.
  • 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
  • 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...
  • 49. MLOps Stack: Model monitoring DIY: Amazon CloudWatch
  • 50. Example of ML solution architecture
  • 51.