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Amazon SageMaker

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Amazon SageMaker

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Amazon SageMaker

  1. 1. Amazon SageMaker
  2. 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. 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. 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. 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. 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. 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. 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. 9. © 2017, Amazon Web Services, Inc. or its Affiliates. All rights reserved. Highly-optimized machine learning algorithms BuildPre-built notebook instances Amazon SageMaker
  10. 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. 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. 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. 13. Digital Globe http://blog.digitalglobe.com/industry/using-machine-learning- to-save-money-on-cloud-data-storage/ https://www.youtube.com/watch?v=mkKkSRIxU8M In the last 18 years DigitalGlobe has been operating Earth imaging satellites, they have collected over 100 PB of imagery. There is a trade-off between how quickly data can be accessed and how much it will cost to store. Working with the ML Lab, Digital Globe built a predictive model that will reduce cloud storage costs for their imagery archive by 50%.
  14. 14. Detecting buildings in Vietnam https://developmentseed.org/blog/2018/01/19/sagemaker-label-maker-case/
  15. 15. © 2017, Amazon Web Services, Inc. or its Affiliates. All rights reserved. Demos 1.Use a built-in algorithm: fine-tuning a pre-trained image classification model 2.Bring your own training code: distributed training from scratch with MXNet and Gluon 3.Bring your own pre-trained model: classifying the Iris data set with a pre-trained TensorFlow model 4.Bring your own container: classifying the Iris data set with Decisions Trees in scikit-learn
  16. 16. © 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
  17. 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 An overview of Amazon SageMaker https://www.youtube.com/watch?v=ym7NEYEx9x4 https://medium.com/@julsimon
  18. 18. Thank you!

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