Machine Learning in azione con Amazon SageMaker
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Il Machine Learning può sembrare più difficile di quanto non lo sia perché il processo di sviluppo, training e deployment dei modelli in produzione è troppo complicato e lento. Amazon SageMaker è un servizio completamente gestito che consente a sviluppatori e data scientist di progettare, implementare e distribuire modelli di Machine Learning in qualsiasi scala. Amazon SageMaker offre una scelta di algoritmi di machine learning altamente performanti e framework preconfigurati come Apache MXNet, TensorFlow, PyTorch e Chainer; inoltre, è possibile utilizzare framework o algoritmi alternativi attraverso container Docker. In questa sessione approfondiremo l’utilizzo di Amazon SageMaker, anche attraverso alcuni pratici esempi.
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Machine Learning in azione con Amazon SageMaker
- 1. © 2018, Amazon Web Services, Inc. or its Affiliates. All rights reserved. Supercharge your Machine Learning Model with Amazon Sagemaker Giuseppe A. Porcelli AWS Solutions Architecture EMEA
- 2. A LONG HISTORY OF ML AT AMAZON THOUSANDS OF ENGINEERS ACROSS THE COMPANY FOCUSED ON AI Personalized recommendations Inventing entirely new customer experiences Fulfillment automation and inventory management Drones Voice-driven interactions
- 3. ML @ AWS OUR MISSION Put Machine Learning in the hands of every developer and data scientist
- 4. APPLICATION SERVICES R E K O G N I T I O N R E K O G N I T I O N V I D E O P O L L Y T R A N S C R I B E T R A N S L A T E C O M P R E H E N D L E X PLATFORMS Amazon SageMaker Amazon Mechanical Turk Spark on Amazon EMR FRAMEWORKS & INFRASTRUCTURE K E R A S F r a m e w o r k s I n t e r f a c e s NVIDIA Tesla V100 GPUs (14x faster than P2) P3 Machine Learning AMIs 5,120 Tensor cores 128GB of memory 1 Petaflop of compute NVLink 2.0 THE AWS MACHINE LEARNING STACK
- 5. Tens of thousands of customers running ML on AWS
- 6. LET’S REVIEW THE ML PROCESS
- 7. THE MACHINE LEARNING PROCESS Business Problem - ML problem framing Set Business Goals • Domain knowledge • Help formulate the right questions
- 8. THE MACHINE LEARNING PROCESS Business Problem - ML problem framing Data Collection Data Integration Data Preparation & Cleaning Build the Data Platform • Amazon S3 • AWS Glue • Amazon Athena • Amazon EMR • Amazon Redshift / Redshift Spectrum • Amazon Kinesis • AWS IoT Core
- 9. THE MACHINE LEARNING PROCESS Data Visualization & Analysis Business Problem - ML problem framing Data Collection Data Integration Data Preparation & Cleaning Feature Engineering Model Training & Parameter Tuning Model Evaluation Experiment, Train, Tune and Evaluate • Setup and manage Notebook Environments • Setup and manage Training Clusters • Write Data Connectors • Scale ML algorithms to large datasets • Distribute ML training algorithm to multiple machines • Secure Model artifacts
- 10. THE MACHINE LEARNING PROCESS Data Visualization & Analysis Business Problem - ML problem framing Data Collection Data Integration Data Preparation & Cleaning Feature Engineering Are Business goals met? Monitoring & Debugging - Predictions Yes Re-training Model Training & Parameter Tuning Model Evaluation Model Deployment Deploy, Monitor and Debug • Setup and manage Model Inference Clusters • Manage and Auto-Scale Model Inference APIs • Monitor and Debug Model Predictions • Models versioning and performance tracking • Automate New Model version promotion to production (A/B testing)
- 11. THE MACHINE LEARNING PROCESS Data Visualization & Analysis Business Problem - ML problem framing Data Collection Data Integration Data Preparation & Cleaning Feature Engineering Are Business goals met? Monitoring & Debugging - Predictions YesNo DataAugmentation Feature Augmentation Re-training Model Training & Parameter Tuning Model Evaluation Model Deployment Enhance and re-train • Add/Remove features • Augment Data
- 12. WHERE SHOULD YOU SPEND YOUR TIME?
- 14. BUILD TRAIN RUN SCALABLE TRAINING
- 16. BUILD TRAIN DEPLOY TUNE DEPLOY AND OPERATE
- 17. Amazon SageMaker A managed service that provides the quickest and easiest way for data scientists and developers to get ML models from idea to production
- 18. AMAZON SAGEMAKER Pre-built notebooks for common problems Built-in, high performance algorithms BUILD TRAIN & TUNE DEPLOY BUILD, TRAIN, TUNE AND HOST YOUR OWN MODELS
- 19. AMAZON SAGEMAKER Pre-built notebooks for common problems Built-in, high performance algorithms One-click training BUILD TRAIN & TUNE DEPLOY Hyperparameter optimization BUILD, TRAIN, TUNE AND HOST YOUR OWN MODELS
- 20. AMAZON SAGEMAKER Fully managed hosting with auto- scaling One-click deployment Pre-built notebooks for common problems Built-in, high performance algorithms One-click training BUILD TRAIN & TUNE DEPLOY Hyperparameter optimization BUILD, TRAIN, TUNE AND HOST YOUR OWN MODELS
- 21. AMAZON SAGEMAKER Fully managed hosting with auto- scaling One-click deployment Pre-built notebooks for common problems Built-in, high performance algorithms One-click training BUILD TRAIN & TUNE DEPLOY End-to-end encryption with KMS End-to-end VPC support Compliance and audit capabilities Metadata and experiment management capabilities Pay as you go Hyperparameter optimization BUILD, TRAIN, TUNE AND HOST YOUR OWN MODELS
- 22. AMAZON SAGEMAKER CUSTOMERS “ - Ashok Srivastava, Chief Data Officer, Intuit With Amazon SageMaker, we can accelerate our Artificial Intelligence initiatives at scale by building and deploying our algorithms on the platform. We will create novel large-scale machine learning and AI algorithms and deploy them on this platform to solve complex problems that can power prosperity for our customers. "
- 23. AMAZON SAGEMAKER @ INTUIT Ad-hoc setup and management of notebook environments Limited choices for model deployment Competing for compute resources across teams Easy data exploration in SageMaker notebooks Building around virtualization for flexibility Auto-scalable model hosting environment From To
- 24. AMAZON SAGEMAKER CUSTOMERS “ - Dr. Walter Scott, CTO of Maxar Technologies and founder of DigitalGlobe " As the world’s leading provider of high-resolution Earth imagery, data and analysis, DigitalGlobe works with enormous amounts of data every day. DigitalGlobe is making it easier for people to find, access, and run compute against our entire 100PB image library, which is stored in AWS’s cloud, to apply deep learning to satellite imagery. We plan to use Amazon SageMaker to train models against petabytes of Earth observation imagery datasets using hosted Jupyter notebooks, so DigitalGlobe's Geospatial Big Data Platform (GBDX) users can just push a button, create a model, and deploy it all within one scalable distributed environment at scale.
- 25. AMAZON SAGEMAKER DEEP DIVE
- 26. AMAZON SAGEMAKER COMPONENTS BUILT-IN ALGORITHMS BRING YOUR OWN SCRIPT BRING YOUR OWN ALGORITHM NOTEBOOK INSTANCES SDKs & LOCAL MODE AWS CONSOLE USER EXPERIENCE ML TRAINING & TUNING SERVICE ML HOSTING SERVICE
- 27. AMAZON SAGEMAKER COMPONENTS BUILT-IN ALGORITHMS BRING YOUR OWN SCRIPT BRING YOUR OWN ALGORITHM NOTEBOOK INSTANCES SDKs & LOCAL MODE AWS CONSOLE USER EXPERIENCE ML TRAINING & TUNING SERVICE ML HOSTING SERVICE
- 28. NOTEBOOK INSTANCES ZERO SETUP FOR EXPLORATORY DATA ANALYSIS Authoring & Notebooks ETL Access to AWS Database services Access to S3 Data Lake VPC • Fully managed Jupyter notebook instances • Choice of CPU and GPU ml instances • Sample notebooks and «just add data» • Recommendations/Personalization • Fraud Detection • Forecasting • Image Classification • Churn Prediction • Marketing Email/Campaign Targeting • Log processing and anomaly detection • Speech to Text • More… • VPC Integration • Lifecycle Configurations
- 29. SDKs AND LOCAL MODE T r a i n w i t h l o c a l n o t e b o o k s Train on notebook instances PetaFLOP training on p3.16xl Go distributed with one line of code Same containers Amazon SageMaker Python SDK https://github.com/aws/sagemaker-python-sdk Amazon SageMaker Spark SDK https://github.com/aws/sagemaker-spark LOCAL MODE
- 30. AWS CONSOLE
- 31. AMAZON SAGEMAKER COMPONENTS BUILT-IN ALGORITHMS BRING YOUR OWN SCRIPT BRING YOUR OWN ALGORITHM NOTEBOOK INSTANCES SDKs & LOCAL MODE AWS CONSOLE USER EXPERIENCE ML TRAINING & TUNING SERVICE ML HOSTING SERVICE
- 32. MANAGED DISTRIBUTED TRAINING Fully managed – VPC– Training Code Training Data Model Artifacts CPU ML INSTANCES GPU ML INSTANCES HYPERPARAMETER TUNING BUILT-IN ALGORITHMS BRING YOUR OWN SCRIPT BRING YOUR OWN ALGORITHM Amazon ECR
- 33. BUILT-IN ALGORITHMS Data Model NEW DATA PREDICTION Algorithm K-Means k-nearest neighbors (k-NN) PCA LDA Factorization Machines Linear Learner NTM RandomCutForest Sequence to Sequence XGBoost Image Classification Object Detection DeepAR Forecasting BlazingText
- 34. BUILT-IN ALGORITHMS Algorithms for “infinite scale” Distributed by default Train on a data stream Checkpoint for re-training Single pass training Not memory bound
- 35. BRING YOUR OWN SCRIPT Data Model NEW DATA PREDICTION Your Own Script +
- 36. BRING YOUR OWN ALGORITHM Data Model NEW DATA PREDICTION Your algorithm and libraries in your own Docker Container
- 37. HYPERPARAMETER TUNING Run a large set of training jobs with varying hyperparameters... ... and search the hyperparameter space for improved accuracy.
- 38. HOSTING Amazon ECR Model Artifacts Inference Image Model Create a Model EASY MODEL DEPLOYMENT TO AMAZON SAGEMAKER
- 39. HOSTING Amazon ECR Model Artifacts Inference Image Model versions Create versions of a Model EASY MODEL DEPLOYMENT TO AMAZON SAGEMAKER
- 40. HOSTING Amazon ECR 30 50 10 10 Model Artifacts Inference Image Model versions InstanceType: ml.c5.4xlarge InitialInstanceCount: 3 maxInstanceCount: 10 ModelName: prod VariantName: primary InitialVariantWeight: 50 Create weighted ProductionVariant(s) ProductionVariant EASY MODEL DEPLOYMENT TO AMAZON SAGEMAKER
- 41. HOSTING Amazon ECR 30 50 10 10 Model Artifacts Inference Image Model versions EndpointConfiguration InstanceType: ml.c5.4xlarge InitialInstanceCount: 3 maxInstanceCount: 10 ModelName: prod VariantName: primary InitialVariantWeight: 50 Create and EndpointConfiguration from one or many ProductionVariant(s) ProductionVariant EASY MODEL DEPLOYMENT TO AMAZON SAGEMAKER
- 42. HOSTING Amazon ECR 30 50 10 10 Model Artifacts Inference Image Model versions EndpointConfiguration Inference Endpoint InstanceType: ml.c5.4xlarge InitialInstanceCount: 3 maxInstanceCount: 10 ModelName: prod VariantName: primary InitialVariantWeight: 50 Create and Endpoint from one EndpointConfiguration ProductionVariant EASY MODEL DEPLOYMENT TO AMAZON SAGEMAKER
- 43. HOSTING Amazon ECR 30 50 10 10 Model Artifacts Inference Image Model versions EndpointConfiguration Inference Endpoint InstanceType: ml.c5.4xlarge InitialInstanceCount: 3 maxInstanceCount: 10 ModelName: prod VariantName: primary InitialVariantWeight: 50 One-click deployment for built-in algorithms and containers ProductionVariant EASY MODEL DEPLOYMENT TO AMAZON SAGEMAKER
- 44. AUTO SCALING
- 45. BATCH TRANSFORM Dataset in S3 bucket AGENT MODEL Instance Node 1 Instance Node n Assembled Data Record Batch Request Data Transformed Data … Cluster
- 46. SAGEMAKER SAMPLE END-TO-END ARCHITECTURE SageMaker Notebooks Training Algorithm SageMaker Training Amazon ECR Code Commit Code Pipeline SageMaker Hosting Coco dataset AWS Lambda API Gateway Build Train Deploy static website hosted on S3 Inference requests Amazon S3 Amazon Cloudfront Web assets on Cloudfront STYLE TRANSFER
- 47. IT’S NOT JUST ABOUT ML Data Lake Storage Amazon S3 Security Access Control Encryption VPC KMS Auditing Compliance Roles Fine Grained Access Controls Compute Powerful GPU & CPU Instances AWS Lambda Analytics Amazon Athena Amazon EMR Amazon Redshift & Redshift Spectrum
- 48. © 2018, Amazon Web Services, Inc. or its Affiliates. All rights reserved. Thank You!