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Amazon SageMaker workshop

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Julien SIMON
Julien SIMON

Tel Aviv, 25/04/2018

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Machine Learning: From Notebook to Production with Amazon Sagemaker Julien Simon Principal Evangelist, Artificial Intelligence & Machine Learning @julsimon April 2018
© 2017, Amazon Web Services, Inc. or its Affiliates. All rights reserved. Platform Services AWS ML Stack Deploy machine learning models with high-performance machine learning algorithms, broad framework support, and one-click training, tuning, and inference.
© 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 YesNo DataAugmentation Feature Augmentation The Machine Learning Process Re-training Predictions
© 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 YesNo DataAugmentation Feature Augmentation Problem discovery Re-training • Help formulate the right questions • Domain Knowledge Predictions
© 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 YesNo DataAugmentation Feature Augmentation Retraining • Need a data platform? • Amazon S3 • AWS Glue • Amazon Athena • Amazon EMR • Amazon Redshift Spectrum Integration Predictions
© 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 YesNo DataAugmentation Feature Augmentation Retraining Model Training Predictions • 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
© 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 YesNo DataAugmentation Feature Augmentation Retraining Model Deployment Predictions • Setup and manage Model Inference Clusters • Manage and Scale Model Inference APIs • Monitor and Debug Model Predictions • Models versioning and performance tracking • Automate New Model version promotion to production (A/B testing)
© 2017, Amazon Web Services, Inc. or its Affiliates. All rights reserved. End-to-End Machine Learning Platform Zero setup Flexible Model Training Pay by the second $ Amazon SageMaker Build, train, and deploy machine learning models at scale
© 2017, Amazon Web Services, Inc. or its Affiliates. All rights reserved. Highly-optimized machine learning algorithms BuildPre-built notebook instances Amazon SageMaker
© 2017, Amazon Web Services, Inc. or its Affiliates. All rights reserved. Highly-optimized machine learning algorithms One-click training for ML, DL, and custom algorithms BuildPre-built notebook instances Easier training with hyperparameter optimization Train Amazon SageMaker
© 2017, Amazon Web Services, Inc. or its Affiliates. All rights reserved. One-click training for ML, DL, and custom algorithms Easier training with hyperparameter optimization Highly-optimized machine learning algorithms Deployment without engineering effort Fully-managed hosting at scale BuildPre-built notebook instances Deploy Train Amazon SageMaker
© 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
© 2017, Amazon Web Services, Inc. or its Affiliates. All rights reserved. Open Source Containers for TF and MXNet https://github.com/aws/sagemaker-tensorflow-containers https://github.com/aws/sagemaker-mxnet-containers • Customize them • Run them locally for development and testing • Run them on SageMaker for training and prediction at scale
© 2017, Amazon Web Services, Inc. or its Affiliates. All rights reserved. Bring your own container https://github.com/aws/sagemaker-container-support • Integration with SageMaker Python SDK Estimators, including: • Downloading user-provided Python code • Deserializing hyperparameters (preserving their Python types) • bin/entry.py, the Docker entrypoint required by SageMaker • Reading in the metadata files provided to the container during training • nginx + Gunicorn HTTP server for serving inference requests https://github.com/awslabs/amazon-sagemaker-examples/tree/master/advanced_functionality/scikit_bring_your_own https://github.com/awslabs/amazon-sagemaker-examples/tree/master/advanced_functionality/r_bring_your_own
© 2017, Amazon Web Services, Inc. or its Affiliates. All rights reserved. End-to-End Machine Learning Platform Zero setup Flexible Model Training Pay by the second $ Amazon SageMaker Build, train, and deploy machine learning models at scale
© 2017, Amazon Web Services, Inc. or its Affiliates. All rights reserved. Example #1: binary classifier with Linear Learner built-in algo https://github.com/awslabs/amazon-sagemaker- examples/tree/master/introduction_to_amazon_algorithms/linear_learner_mnist Example #2: multi-class classifier with XGBoost built-in algo (low-level API) https://github.com/awslabs/amazon- sagemaker-examples/tree/master/introduction_to_amazon_algorithms/xgboost_mnist Example #3: bring your own code (TensorFlow or MXNet, we should let the participants choose) • https://github.com/awslabs/amazon-sagemaker-examples/tree/master/sagemaker-python- sdk/tensorflow_distributed_mnist • https://github.com/awslabs/amazon-sagemaker-examples/tree/master/sagemaker-python- sdk/mxnet_gluon_sentiment Example #4: bring your own model (TensorFlow) and deploy it on SageMaker: https://github.com/awslabs/amazon- sagemaker-examples/tree/master/advanced_functionality/tensorflow_iris_byom Example #5 (optional): bring your own container https://github.com/awslabs/amazon-sagemaker- examples/blob/master/advanced_functionality/scikit_bring_your_own/scikit_bring_your_own.ipynb
© 2017, Amazon Web Services, Inc. or its Affiliates. All rights reserved. Resources https://aws.amazon.com/machine-learning https://aws.amazon.com/blogs/ai https://aws.amazon.com/sagemaker https://github.com/awslabs/amazon-sagemaker-examples https://github.com/aws/sagemaker-python-sdk An overview of Amazon SageMaker https://www.youtube.com/watch?v=ym7NEYEx9x4 https://medium.com/@julsimon
Thank you! Julien Simon Principal Evangelist, Artificial Intelligence & Machine Learning @julsimon

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Amazon SageMaker workshop

  • 1. Machine Learning: From Notebook to Production with Amazon Sagemaker Julien Simon Principal Evangelist, Artificial Intelligence & Machine Learning @julsimon April 2018
  • 2. © 2017, Amazon Web Services, Inc. or its Affiliates. All rights reserved. Platform Services AWS ML Stack Deploy machine learning models with high-performance machine learning algorithms, broad framework support, and one-click training, tuning, and inference.
  • 3. © 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 YesNo DataAugmentation Feature Augmentation The Machine Learning Process Re-training Predictions
  • 4. © 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 YesNo DataAugmentation Feature Augmentation Problem discovery Re-training • Help formulate the right questions • Domain Knowledge Predictions
  • 5. © 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 YesNo DataAugmentation Feature Augmentation Retraining • Need a data platform? • Amazon S3 • AWS Glue • Amazon Athena • Amazon EMR • Amazon Redshift Spectrum Integration Predictions
  • 6. © 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 YesNo DataAugmentation Feature Augmentation Retraining Model Training Predictions • 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
  • 7. © 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 YesNo DataAugmentation Feature Augmentation Retraining Model Deployment Predictions • Setup and manage Model Inference Clusters • Manage and Scale Model Inference APIs • Monitor and Debug Model Predictions • Models versioning and performance tracking • Automate New Model version promotion to production (A/B testing)
  • 8. © 2017, Amazon Web Services, Inc. or its Affiliates. All rights reserved. End-to-End Machine Learning Platform Zero setup Flexible Model Training Pay by the second $ Amazon SageMaker Build, train, and deploy machine learning models at scale
  • 9. © 2017, Amazon Web Services, Inc. or its Affiliates. All rights reserved. Highly-optimized machine learning algorithms BuildPre-built notebook instances Amazon SageMaker
  • 10. © 2017, Amazon Web Services, Inc. or its Affiliates. All rights reserved. Highly-optimized machine learning algorithms One-click training for ML, DL, and custom algorithms BuildPre-built notebook instances Easier training with hyperparameter optimization Train Amazon SageMaker
  • 11. © 2017, Amazon Web Services, Inc. or its Affiliates. All rights reserved. One-click training for ML, DL, and custom algorithms Easier training with hyperparameter optimization Highly-optimized machine learning algorithms Deployment without engineering effort Fully-managed hosting at scale BuildPre-built notebook instances Deploy Train Amazon SageMaker
  • 12. © 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
  • 13. © 2017, Amazon Web Services, Inc. or its Affiliates. All rights reserved. Open Source Containers for TF and MXNet https://github.com/aws/sagemaker-tensorflow-containers https://github.com/aws/sagemaker-mxnet-containers • Customize them • Run them locally for development and testing • Run them on SageMaker for training and prediction at scale
  • 14. © 2017, Amazon Web Services, Inc. or its Affiliates. All rights reserved. Bring your own container https://github.com/aws/sagemaker-container-support • Integration with SageMaker Python SDK Estimators, including: • Downloading user-provided Python code • Deserializing hyperparameters (preserving their Python types) • bin/entry.py, the Docker entrypoint required by SageMaker • Reading in the metadata files provided to the container during training • nginx + Gunicorn HTTP server for serving inference requests https://github.com/awslabs/amazon-sagemaker-examples/tree/master/advanced_functionality/scikit_bring_your_own https://github.com/awslabs/amazon-sagemaker-examples/tree/master/advanced_functionality/r_bring_your_own
  • 15. © 2017, Amazon Web Services, Inc. or its Affiliates. All rights reserved. End-to-End Machine Learning Platform Zero setup Flexible Model Training Pay by the second $ Amazon SageMaker Build, train, and deploy machine learning models at scale
  • 16. © 2017, Amazon Web Services, Inc. or its Affiliates. All rights reserved. Example #1: binary classifier with Linear Learner built-in algo https://github.com/awslabs/amazon-sagemaker- examples/tree/master/introduction_to_amazon_algorithms/linear_learner_mnist Example #2: multi-class classifier with XGBoost built-in algo (low-level API) https://github.com/awslabs/amazon- sagemaker-examples/tree/master/introduction_to_amazon_algorithms/xgboost_mnist Example #3: bring your own code (TensorFlow or MXNet, we should let the participants choose) • https://github.com/awslabs/amazon-sagemaker-examples/tree/master/sagemaker-python- sdk/tensorflow_distributed_mnist • https://github.com/awslabs/amazon-sagemaker-examples/tree/master/sagemaker-python- sdk/mxnet_gluon_sentiment Example #4: bring your own model (TensorFlow) and deploy it on SageMaker: https://github.com/awslabs/amazon- sagemaker-examples/tree/master/advanced_functionality/tensorflow_iris_byom Example #5 (optional): bring your own container https://github.com/awslabs/amazon-sagemaker- examples/blob/master/advanced_functionality/scikit_bring_your_own/scikit_bring_your_own.ipynb
  • 17. © 2017, Amazon Web Services, Inc. or its Affiliates. All rights reserved. Resources https://aws.amazon.com/machine-learning https://aws.amazon.com/blogs/ai https://aws.amazon.com/sagemaker https://github.com/awslabs/amazon-sagemaker-examples https://github.com/aws/sagemaker-python-sdk An overview of Amazon SageMaker https://www.youtube.com/watch?v=ym7NEYEx9x4 https://medium.com/@julsimon
  • 18. Thank you! Julien Simon Principal Evangelist, Artificial Intelligence & Machine Learning @julsimon