[DSC Adria 23] Mikhail Rozhkov DVC in Machine Learning Engineering and MLOps practices.pdf
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"This talk discusses the landscape of features and uses cases of DVC in ML engineering and MLOps: - Get started with versioning data, artifacts, and models with Data Version Control (DVC), - Automated and reproducible pipelines with DVC, - Experiment management and metrics tracking with DVC, - DVC in MLOps practices"
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[DSC Adria 23] Mikhail Rozhkov DVC in Machine Learning Engineering and MLOps practices.pdf
- 1. Machine Learning Engineering and MLOps practices with Data Version Control (DVC) 1 Mikhail Rozhkov Machine Learning REPA: mlrepa.org DSC ADRIA 2023
- 2. About me Mikhail Rozhkov @mnrozhkov About : ➔ ML Engineer and MLOps Consultant ➔ Founder of the mlrepa.org community Expertise: ➔ Automation & MLOps ➔ ML Engineering Training programs: ➔ Data Versioning and Pipelines automation with DVC ➔ MLOps for Batch Scoring (telecom, banking, retail) ➔ MLOps for Computer Vision and NLP
- 3. Agenda 3 ➔ Why should we invest in MLOps? ➔ Overview DVC for ML ◆ Version control ◆ Experiments and metrics tracking ◆ Automated pipelines ➔ DVC in MLOps practices
- 4. Why should we invest in MLOps?
- 5. What is MLOps? Source: Practitioners Guide to MLOps (Google) MLOps is a set of standardized processes and technology capabilities for building, deploying, and operationalizing ML systems rapidly and reliably Source: Practitioners Guide to MLOps (Google)
- 6. What is MLOps? MLOps is a set of standardized processes and technology capabilities for building, deploying, and operationalizing ML systems rapidly and reliably Source: Practitioners Guide to MLOps (Google) ➔ Get a real value from ML ➔ Fast Time-to-Market ➔ Reproducibility and Reliability ➔ Maintainability ➔ Cost-Efficient ➔ Maximize real value from ML ➔ Fast Time-to-Market ➔ Reproducibility and Reliability ➔ Maintainability ➔ Cost Effective
- 7. MLOps: An end-to-end workflow Source: Practitioners Guide to MLOps (Google)
- 8. Costs Value What is MLOps? Development Generated value Reproducibility and Reliability Maintainability Deployment (Integration) & Operation Computing Resources Fast Time-to-Market Maximize real value from ML Cost Effective Operational Risks
- 9. Overview DVC in Machine Learning
- 10. DVC features: Versioning data, artifacts, and models
- 11. How DVC works with data? 11 Original image source: https://dvc.org/doc/use-cases/data-and-model-files-versioning dataset 10 GB
- 12. dataset 10 GB Store data in remote storage 12 remote git repository remote data cache
- 13. dataset 10 GB Bring data to local workspace 13 local git repository local cache
- 14. Simplify a team collaboration 14 Image source: https://dvc.org/doc/use-cases/data-and-model-files-versioning
- 15. DVC features: Data and ML pipelines automation
- 16. Configure pipelines in a simple dvc.yaml Load Data Split Data Train Model Evaluation data_load.py data_split.py train.py eval.py Source: Alex Kim, Optimizing Image Segmentation Projects with DVC, Iterative.ai
- 17. Use any executable script as a stage job data_load.py data_split.py train.py eval.py Jupyter Notebook Python module Docker container Any script (bash)
- 18. test_data.csv train_data.csv data_load 18 feature _extraction evaluate train Evaluation Report Model - artifacts - pipelines Run only stages that need to be run raw_data.csv Run as simple as: dvc exp run
- 19. DVC features: Experiment management and metrics tracking
- 20. Track Experiments in CLI https://iterative.ai/blog/DVC-VS-Code-extension dvc exp show to visualize metrics dvc exp push to save (commit) experiment
- 21. …or, use DVC extension UI in VSCode https://iterative.ai/blog/DVC-VS-Code-extension No metrics tracking server is required!
- 22. All experiments are versioned Experiment Tracking Code & Data Versioning Experiment Versioning
- 23. DVC in MLOps practices
- 24. How does DVC help in MLOps practices? Source: Practitioners Guide to MLOps (Google) DVC ??? DVC ??? DVC ??? DVC ??? DVC ???
- 25. DVC in MLOps: Easy work with Data and Models Source: Practitioners Guide to MLOps (Google) Data Registry Model Registry Reproducibility and Reliability Maintainability Fast Time-to-Market Cost Effective
- 26. Run Experiments Hyper-parameter s Tuning Validation Datasets Management Run experiments in Clouds Train production models Reproducibility and Reliability Maintainability Fast Time-to-Market Maximize real value from ML Cost Effective DVC in MLOps: Automated & reproducible pipelines, easy to maintain
- 27. DVC in MLOps: Speed up deployment and monitoring Manage Monitoring Artifacts Source: Practitioners Guide to MLOps (Google) Access artifacts for deployment Manage Monitoring Artifacts Maintainability Fast Time-to-Market
- 28. Summary
- 29. ➔ DVC is a “swiss knife” for ML projects ◆ Experiment, Train, Deploy, Monitor ➔ Data Versioning + Code Versioning -> Reproducibility ➔ Automated ML pipelines -> Faster Time-to-Market ➔ Smart versioning -> Reduce computation costs ➔ Open source -> Flexible & Customisable 29 Summary
- 30. mlrepa.org Thank you! Machine Learning REPA Community