Cloud Computing: New era in ML. Gianluigi Mucciolo - XPeppers
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Tech 5. May 18th 2018. Data Driven Innovation 2018. Engineering Department, University of Roma Tre
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Cloud Computing: New era in ML. Gianluigi Mucciolo - XPeppers
- 1. Nome Speaker @twitter Cloud Computing: New era in ML Gianluigi Mucciolo /mucciolo91
- 3. Raph C. Merkle Raymond Kurzweil PaulReber
- 4. The Machine Learning Process Business Problem Re-training Predictions No Yes DataAugmentation Feature Augmentation Are Business Goals met?
- 5. Knowledge Time Computation Money
- 9. AI Frameworks AWS Deep Learning AMI • Easy-to-launch tutorials • Hassle-free setup and configuration • Pay only for what you use • Accelerate your model training and deployment • Support for popular deep learning frameworks
- 10. AI Services
- 12. Business Problem Re-training Integration: Data Architecture Predictions No Yes DataAugmentation Feature Augmentation Are Business Goals met?
- 13. Business Problem Re-training Modelling: The Data Science Predictions No Yes DataAugmentation Feature Augmentation Are Business Goals met?
- 14. Business Problem Re-training Production: The DevOps Predictions No Yes DataAugmentation Feature Augmentation Are Business Goals met?
- 15. Amazon SageMaker is a fully managed service that enable developers and data scientist to quickly and easilybuild, train anddeploymachine learning models at any scale.. ..from idea to production.
- 17. • Recommendations • Personalization • Fraud Detection • Forecasting • Image Classification • Churn Prediction • Marketing Email • Campaign Targeting • Log processing • Anomaly detection • Speech to Text • More… “Just add data”
- 20. 4 ML Hosting Service Different Versions of the same inference code in ECR. Prod is the master container, it will serve over 50% of requests. Amazon ECR Model Artifacts
- 21. 4 ML Hosting Service Amazon ECR Model Artifacts ModelName: prod Create a Model Different Versions of the same inference code in ECR. Prod is the master container, it will serve over 50% of requests.
- 22. 4 ML Hosting Service Amazon ECR Model Artifacts Model Versions Create a versions of a Model Different Versions of the same inference code in ECR. Prod is the master container, it will serve over 50% of requests.
- 23. 4 ML Hosting Service InstanceType: c3.4xlarge MinInstanceCount: 5 MaxInstanceCount: 20 ModelNmae: prod VariantName: prodPrimary VariantWeight: 50 ProductionVariant Amazon ECR Model Artifacts 30 50 10 10 Model Versions Create weighted ProductionVariants Different Versions of the same inference code in ECR. Prod is the master container, it will serve over 50% of requests.
- 24. 4 ML Hosting Service InstanceType: c3.4xlarge MinInstanceCount: 5 MaxInstanceCount: 20 ModelName: prod VariantName: prodPrimary VariantWeight: 50 ProductionVariant Amazon ECR Model Artifacts Endpoint Configuration 30 50 10 10 Model Versions Create an EndpointConfiguration from one or many ProductionVariants Different Versions of the same inference code in ECR. Prod is the master container, it will serve over 50% of requests.
- 25. 4 ML Hosting Service InstanceType: c3.4xlarge MinInstanceCount: 5 MaxInstanceCount: 20 ModelNmae: prod VariantName: prodPrimary VariantWeight: 50 ProductionVariant Amazon ECR Model Artifacts Endpoint Configuration Inference Endpoint 30 50 10 10 Model Versions Create an Endpoint from one EndpointConfiguration Different Versions of the same inference code in ECR. Prod is the master container, it will serve over 50% of requests.
- 26. • Training Algorithm / inference code is packaged in Docker Image on ECR • SageMaker pulls training algorithm image from ECR into Model raining Service • Amazon SM downloads or streams the training data and runs Training • After training Amazon SM uploads model artifacts to Amazon S3 • For Inference, Amazon SM pulls the model artifacts and the inference image from ECR into Model Hosting Service • Amazon SM exposes an inference endpoint for client application to send prediction requests • Ground truth data collected from the client could be sent into training bucket to retrain and update the model
- 27. 4
- 29. Demo • Build Workflow • Addestramento • Deploy