How to Build a ML Platform Efficiently Using Open-Source
Fast-growing startups usually face a common set of challenges when employing machine learning. Data scientists are expected to work on new products and develop new models as well as iterate on existing ones. Once in production, models should be continuously monitored and regularly maintained as the infrastructure evolves. Before too long, data scientists end up spending most of their time doing maintenance and firefighting of existing models instead of creating new ones. At GetYourGuide, we faced these challenges and decided to think about machine learning development holistically, which led us to our machine learning platform. Our platform uses MLflow to keep track of our machine learning life-cycle and ease the development experience. To integrate our models into our production environment, we also need to deal with additional requirements like API specification, SLOs and monitoring. To empower our data scientists, we have built a templating system that takes care of the heavy lifting of going to production, leveraging software engineering tools and ML-specific ones like BentoML. In this talk we will present: – Our previous approaches for deploying models and their tradeoffs – Our data science and platform principles – The main functionalities of our platform – A live demo to create a new service – Our learnings in the process
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How to Build a ML Platform Efficiently Using Open-Source
- 1. How to Build a ML Platform Efficiently Using Open-Source Jean Carlo Machado Theodore Meynard GetYourGuide 1
- 2. Agenda ▪ Introduction ▪ ML at GetYourGuide ▪ Before the Platform ▪ ML Platform ▪ Demo ▪ Final words 2
- 3. Who Are We Theo: Senior Data Scientist Jean: Senior Software Engineer 3 theodore.meynard@getyourguide.com jean.machado@getyourguide.com
- 5. We’ve built the world’s largest marketplace for travel activities… Millions of travelers use GetYourGuide every year We facilitate the transaction We offer more than 40,000 activities worldwide Connecting customers... ...to suppliers around the world 5
- 7. Data Product: Ranking Service 7
- 8. Data Product: Recommendation Panels 8
- 9. Data Product: Paid Search 9
- 10. Amsterdam : Canal Cruise Other Data Products We also use ML for: • Demand forecasting • Inventory labeling ⇒ 20+ ML projects distributed in 2 teams + delivered models to other teams to maintain 10
- 11. Data Product Principles We follow clean code principles PoCs are temporary We build solid, resilient deployment processes Know our models health at every point in time Quality, Testing & Monitoring We integrate into the engineering ecosystem and leverage open-source Data workflows are efficient and cost-effective We take reproducibility and modularity seriously Engineering We Promote the Data Product mindset We deeply integrate with data stakeholders Stakeholder Engagement We actively manage the unknowns in our planning Data analytics dynamically informs our project plans Workflow Customer and business value over fancy solutions Exploration is one of our goals Strategy We value small iterations on existing models We value explainability over pure accuracy Performance is proven online Model Our principles explained 11
- 13. How We Started Pros ● Widely used by ML practitioners ● Good to start new projects & prototype ● Great visualization Cons ● No proper version control ● No code reuse ● No automatic testing 13
- 14. A Major Improvement Pros ● Tests included in library ● Version control with code review ● Maintainable projects Cons ● No CI/CD ● No model tracking 14
- 15. ML Platform 15
- 16. From Amsterdam: Volendam, Marken and Windmills ML Platform Key Features ● CI/CD ● Model Tracking ● Batch & Online inference 16
- 17. ML Platform Principles • Maximize data scientist’s model ownership • Reproducible Machine Learning • Reuse most of our existing infrastructure • Build incrementally • Use open-source and open standards 17
- 19. ML CI/CD 19
- 20. Training Path 20
- 23. Online Inference With BentoML 23
- 24. From MLflow to BentoML Example import mlflow from iris_classifier import IrisClassifier # Load mlflow model mlflow_model = mlflow.sklearn.load_model(model_uri) # Create a iris classifier service instance iris_classifier_service = IrisClassifier() # Pack the newly trained model artifact iris_classifier_service.pack("model", mlflow_model) # Save the prediction service for model serving saved_path = iris_classifier_service.save() from bentoml import env, artifacts, api, BentoService from bentoml.adapters import DataframeInput from bentoml.frameworks.sklearn import SklearnModelArtifact @artifacts([SklearnModelArtifact("model")]) class IrisClassifier(BentoService): """A minimum prediction service""" @api(input=DataframeInput()) def predict(self, df: pd.DataFrame): """An inference API named `predict`""" return self.artifacts.model.predict(df) iris_classifier.py mlflow2bentoml.py 24
- 25. Demo 25
- 26. Final remarks 26
- 27. ● The integration with the existing architecture fosters proactive collaboration ● The tool space is very new making the exploration vital but time consuming ● A design review is necessary to align everyone Learnings 27
- 28. Amsterdam: Moco Museum Conclusion ● As we grew, we needed to refine our Data science process ● Software engineering good practices + special twist for ML ● Our platform helps Data Scientists to ○ Build faster ○ Deploy safer ○ Document automatically 28
- 29. Feedback Your feedback is important to us. Don’t forget to rate and review the sessions. 29