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![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](https://image.slidesharecdn.com/59machadomeynard-210615153942/75/How-to-Build-a-ML-Platform-Efficiently-Using-Open-Source-24-2048.jpg)
![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](https://crownmelresort.com/image.slidesharecdn.com/59machadomeynard-210615153942/75/How-to-Build-a-ML-Platform-Efficiently-Using-Open-Source-24-2048.jpg)
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How to Build a ML Platform Efficiently Using Open-Source
The document outlines the development of a machine learning (ML) platform at GetYourGuide, detailing their approach to integrating ML within their engineering ecosystem and emphasizing principles such as reproducibility, model ownership, and the use of open-source tools. It highlights the initial challenges faced before the platform's implementation and describes the platform's key features like CI/CD, model tracking, and inference methods. The final remarks stress the importance of collaboration, design reviews, and feedback in refining their data science processes.
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How to Build a ML Platform Efficiently Using Open-Source
- 1. How to Builda ML Platform Efficiently Using Open-Source Jean Carlo Machado Theodore Meynard GetYourGuide 1
- 2. Agenda ▪ Introduction ▪ MLat 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
- 4.
- 5. We’ve built theworld’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
- 6.
- 7. Data Product: RankingService 7
- 8. Data Product: RecommendationPanels 8
- 9. Data Product: PaidSearch 9
- 10. Amsterdam : Canal Cruise Other Data Products Wealso 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 Wefollow 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
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- 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.
- 16. From Amsterdam: Volendam, Markenand 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
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- 23. Online Inference WithBentoML 23
- 24. From MLflow toBentoML 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
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- 27. ● The integrationwith 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 ● Aswe 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 isimportant to us. Don’t forget to rate and review the sessions. 29