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NEW LAUNCH! Integrating Amazon SageMaker into your Enterprise - MCL345 - re:Invent 2017

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Amazon SageMaker is a fully managed platform for data scientists and developers to build, train and deploy machine learning models in production applications. In this workshop, you will learn how to integrate Amazon SageMaker with other AWS services in order to meet enterprise requirements. Using Amazon S3, Amazon Glue, Amazon KMS, Amazon SageMaker, Amazon CodeStar, Amazon ECR, IAM; we will walkthrough the machine learning lifecycle in an integrated AWS environment and discuss best practices.Attendees must have some familiarities with AWS products as well as a good understanding of machine learning theory. The dataset for the workshop will be provided.

NEW LAUNCH! Integrating Amazon SageMaker into your Enterprise - MCL345 - re:Invent 2017

  1. 1. @dmbanga© 2017, Amazon Web Services, Inc. or its Affiliates. All rights reserved. @dmbanga AWS re:INVENT Amazon SageMaker in AWS D a n R . M b a n g a B u s i n e s s D e v e l o p m e n t M a n a g e r A I P l a t f o r m s a n d E n g i n e s M C L 3 4 5 November 29, 2017
  2. 2. @dmbanga© 2017, Amazon Web Services, Inc. or its Affiliates. All rights reserved. Thank You
  3. 3. @dmbanga© 2017, Amazon Web Services, Inc. or its Affiliates. All rights reserved. Solving Some Of The Hardest Problems In Computer Science Learning Language Perception Problem Solving Reasoning
  4. 4. @dmbanga© 2017, Amazon Web Services, Inc. or its Affiliates. All rights reserved. Data Visualization & Analysis Business Problem – ML problem framing Data Collection Data Integration Data Preparation & Cleaning Feature Engineering Model Training & Parameter Tuning Model Evaluation Are Business Goals met? Model Deployment Monitoring & Debugging – Predictions YesNo DataAugmentation Feature Augmentation The Machine Learning Process Re-training
  5. 5. @dmbanga© 2017, Amazon Web Services, Inc. or its Affiliates. All rights reserved. Data Visualization & Analysis Business Problem – ML problem framing Data Collection Data Integration Data Preparation & Cleaning Feature Engineering Model Training & Parameter Tuning Model Evaluation Are Business Goals met? Model Deployment Monitoring & Debugging – Predictions YesNo DataAugmentation Feature Augmentation Discovery: The Analysts Re-training • Help formulate the right questions • Domain Knowledge
  6. 6. @dmbanga© 2017, Amazon Web Services, Inc. or its Affiliates. All rights reserved. Data Visualization & Analysis Business Problem – ML problem framing Data Collection Data Integration Data Preparation & Cleaning Feature Engineering Model Training & Parameter Tuning Model Evaluation Are Business Goals met? Model Deployment Monitoring & Debugging – Predictions YesNo DataAugmentation Feature Augmentation Integration: The Data Architecture Retraining • Build the data platform: • Amazon S3 • AWS Glue • Amazon Athena • Amazon EMR • Amazon Redshift Spectrum
  7. 7. @dmbanga© 2017, Amazon Web Services, Inc. or its Affiliates. All rights reserved. Data Visualization & Analysis Business Problem – ML problem framing Data Collection Data Integration Data Preparation & Cleaning Feature Engineering Model Training & Parameter Tuning Model Evaluation Are Business Goals met? Model Deployment Monitoring & Debugging – Predictions YesNo DataAugmentation Feature Augmentation Retraining Modeling: The Data Science • Builds the ML Models: • AWS Deep Learning AMI • SparkML on Amazon EMR • Amazon SageMaker
  8. 8. @dmbanga© 2017, Amazon Web Services, Inc. or its Affiliates. All rights reserved. Data Visualization & Analysis Business Problem – ML problem framing Data Collection Data Integration Data Preparation & Cleaning Feature Engineering Model Training & Parameter Tuning Model Evaluation Are Business Goals met? Model Deployment Monitoring & Debugging – Predictions YesNo DataAugmentation Feature Augmentation Retraining Production: The DevOps • Build Smart Apps • AWS Lambda • Amazon S3 • API Gateway • IoT • Kinesis • ECS/ECR • Mobile Hub • AWS KMS • EC2 • Amazon SageMaker
  9. 9. @dmbanga© 2017, Amazon Web Services, Inc. or its Affiliates. All rights reserved. A fully managed service that enables data scientists and developers to quickly and easily build machine-learning based models into production smart applications. Amazon SageMaker
  10. 10. @dmbanga© 2017, Amazon Web Services, Inc. or its Affiliates. All rights reserved.  Agile, Reliable, GPU powered, and Productivity Ready Notebook instances for Data Scientist and Developers  High-Performance Web-Scale Algorithms Out Of The Box  Managed Distributed Model Training Service  Production Ready Model Hosting requiring no engineering Amazon SageMaker
  11. 11. @dmbanga© 2017, Amazon Web Services, Inc. or its Affiliates. All rights reserved. Amazon ECR Model Training (on EC2) Amazon SageMaker Client application Training code
  12. 12. @dmbanga© 2017, Amazon Web Services, Inc. or its Affiliates. All rights reserved. Amazon ECR Model Training (on EC2) Trainingdata Training code Helper code Client application Training code Amazon SageMaker
  13. 13. @dmbanga© 2017, Amazon Web Services, Inc. or its Affiliates. All rights reserved. Amazon ECR Model Training (on EC2) Trainingdata Modelartifacts Training code Helper code Client application Inference code Training code Amazon SageMaker
  14. 14. @dmbanga© 2017, Amazon Web Services, Inc. or its Affiliates. All rights reserved. Amazon ECR Model Training (on EC2) Model Hosting (on EC2) Trainingdata Modelartifacts Training code Helper code Helper codeInference code Client application Inference code Training code Amazon SageMaker
  15. 15. @dmbanga© 2017, Amazon Web Services, Inc. or its Affiliates. All rights reserved. Amazon ECR Model Training (on EC2) Model Hosting (on EC2) Trainingdata Modelartifacts Training code Helper code Helper codeInference code Client application Inference code Training code Inference requestInference response Inference Endpoint Amazon SageMaker
  16. 16. @dmbanga© 2017, Amazon Web Services, Inc. or its Affiliates. All rights reserved. Amazon ECR Model Training (on EC2) Model Hosting (on EC2) Trainingdata Modelartifacts Training code Helper code Helper codeInference code GroundTruth Client application Inference code Training code Inference requestInference response Inference Endpoint Amazon SageMaker
  17. 17. @dmbanga© 2017, Amazon Web Services, Inc. or its Affiliates. All rights reserved. Amazon SageMaker 1 2 3 4 I I I I Notebook Instances 1P Algorithms ML Training Service ML Hosting Service
  18. 18. @dmbanga© 2017, Amazon Web Services, Inc. or its Affiliates. All rights reserved. 1 I Notebook Instances Zero Setup For Exploratory Data Analysis Authoring & Notebooks ETL Access to AWS Database services Access to S3 Data Lake • Recommendations/Personalization • Fraud Detection • Forecasting • Image Classification • Churn Prediction • Marketing Email/Campaign Targeting • Log processing and anomaly detection • Speech to Text • More… “Just add data”
  19. 19. @dmbanga© 2017, Amazon Web Services, Inc. or its Affiliates. All rights reserved. 2 I 1P Algorithms ML Algorithms optimized for speed and Large datasets Training code • Matrix Factorization • Regression • Principal Component Analysis • K-Means Clustering • Gradient Boosted Trees • And More! Amazon provided Algorithms Bring Your Own Script (IM builds the Container) IM Estimators in Apache Spark
  20. 20. @dmbanga© 2017, Amazon Web Services, Inc. or its Affiliates. All rights reserved. Managed Distributed Training With Flexibility Training code • Matrix Factorization • Regression • Principal Component Analysis • K-Means Clustering • Gradient Boosted Trees • And More! Amazon provided Algorithms Bring Your Own Script (IM builds the Container) Bring Your Own Algorithm (You build the Container) 3 I ML Training Service Fetch Training data Save Model Artifacts Fully managed – Secured– Amazon ECR Save Inference Image IM Estimators in Apache Spark CPU GPU HPO
  21. 21. @dmbanga© 2017, Amazon Web Services, Inc. or its Affiliates. All rights reserved. 4 I ML Hosting Service Amazon ECR Amazon SageMaker Easy Model Deployment to Amazon SageMaker Versions of the same inference code saved in inference containers. Prod is the primary one, 50% of the traffic must be served there!
  22. 22. @dmbanga© 2017, Amazon Web Services, Inc. or its Affiliates. All rights reserved. 4 I ML Hosting Service Amazon ECR Model Artifacts Inference Image Versions of the same inference code saved in inference containers. Prod is the primary one, 50% of the traffic must be served there! Create a Model ModelName: prod Amazon SageMaker Easy Model Deployment to Amazon SageMaker
  23. 23. @dmbanga© 2017, Amazon Web Services, Inc. or its Affiliates. All rights reserved. 4 I ML Hosting Service Amazon ECR Model Artifacts Inference Image Model versions Versions of the same inference code saved in inference containers. Prod is the primary one, 50% of the traffic must be served there! Create versions of a Model Amazon SageMaker Easy Model Deployment to Amazon SageMaker
  24. 24. @dmbanga© 2017, Amazon Web Services, Inc. or its Affiliates. All rights reserved. 4 I ML Hosting Service Amazon ECR 30 50 10 10 Instance type: c3.4xlarge MinInstanceCount: 5 MaxInstanceCount: 20 ModelName: prod VariantName: prodPrimary VariantWeight: 50 ProductionVariant Model Artifacts Inference Image Model versions Versions of the same inference code saved in inference containers. Prod is the primary one, 50% of the traffic must be served there! Create weighted ProductionVariants Amazon SageMaker Easy Model Deployment to Amazon SageMaker
  25. 25. @dmbanga© 2017, Amazon Web Services, Inc. or its Affiliates. All rights reserved. 4 I ML Hosting Service Amazon ECR 30 50 10 10 InstanceType: c3.4xlarge MinInstanceCount: 5 MaxInstanceCount: 20 ModelName: prod VariantName: prodPrimary VariantWeight: 50 ProductionVariant Model Artifacts Inference Image Model versions Versions of the same inference code saved in inference containers. Prod is the primary one, 50% of the traffic must be served there! Create an EndpointConfiguration from one or many ProductionVariant(s)EndpointConfiguration Amazon SageMaker Easy Model Deployment to Amazon SageMaker
  26. 26. @dmbanga© 2017, Amazon Web Services, Inc. or its Affiliates. All rights reserved. 4 I ML Hosting Service Amazon ECR 30 50 10 10 InstanceType: c3.4xlarge MinInstanceCount: 5 MaxInstanceCount: 20 ModelName: prod VariantName: prodPrimary VariantWeight: 50 ProductionVariant Model Artifacts Inference Image Model versions Versions of the same inference code saved in inference containers. Prod is the primary one, 50% of the traffic must be served there! Create an Endpoint from one EndpointConfiguration EndpointConfiguration Inference Endpoint Amazon SageMaker Easy Model Deployment to Amazon SageMaker
  27. 27. @dmbanga© 2017, Amazon Web Services, Inc. or its Affiliates. All rights reserved. 4 I ML Hosting Service Amazon ECR 30 50 10 10 InstanceType: c3.4xlarge MinInstanceCount: 5 MaxInstanceCount: 20 ModelName: prod VariantName: prodPrimary VariantWeight: 50 ProductionVariant Model Artifacts Inference Image Model versions Versions of the same inference code saved in inference containers. Prod is the primary one, 50% of the traffic must be served there! One-Click! EndpointConfiguration Inference Endpoint Amazon Provided Algorithms Amazon SageMaker Easy Model Deployment to Amazon SageMaker
  28. 28. @dmbanga© 2017, Amazon Web Services, Inc. or its Affiliates. All rights reserved. 4 I ML Hosting Service  Auto-Scaling Inference APIs  A/B Testing (more to come)  Low Latency & High Throughput  Bring Your Own Model  Python SDK Amazon SageMaker Easy Model Deployment to Amazon SageMaker
  29. 29. @dmbanga© 2017, Amazon Web Services, Inc. or its Affiliates. All rights reserved. Let’s build <>!
  30. 30. @dmbanga© 2017, Amazon Web Services, Inc. or its Affiliates. All rights reserved.
  31. 31. @dmbanga© 2017, Amazon Web Services, Inc. or its Affiliates. All rights reserved. SageMaker Notebooks Training Algorithm SageMaker Training Amazon ECR Code Commit Code Pipeline SageMaker Hosting Coco dataset http://sm-demo2017.s3-website-us-east-1.amazonaws.com/ AWS Lambda API Gateway PyTorch on SageMaker Style Transfer App Architecture
  32. 32. @dmbanga© 2017, Amazon Web Services, Inc. or its Affiliates. All rights reserved. @dmbanga THANK YOU! d m m b a n g a @ a m a z o n . c o m

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