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Amazon SageMaker

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2018/4/18 に開催された,AWS Machine Learning Tech Night - April 2018 - の Sunil Mallya による Amazon SageMaker についての発表資料です.
https://awsj-ml.connpass.com/event/83623/

Publié dans : Technologie
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Amazon SageMaker

  1. 1. © 2018, Amazon Web Services, Inc. or its Affiliates. All rights reserved. Amazon SageMaker Sunil Mallya Sr. AI Solutions Architect/ ML Solutions Lab / Amazon Web Services
  2. 2. © 2018, 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 Build Pre-built notebook instances Deploy Train Amazon SageMaker
  3. 3. © 2018, Amazon Web Services, Inc. or its Affiliates. All rights reserved. Training code • Matrix Factorization • Regression • Principal Component Analysis • K-Means Clustering • Gradient Boosted Trees • And More! Amazon provided Algorithms Bring Your Own Container Bring Your Own Script IM Estimators in Apache Spark Amazon SageMaker: model options
  4. 4. © 2018, Amazon Web Services, Inc. or its Affiliates. All rights reserved. Streaming datasets, for cheaper training Train faster, in a single pass Greater reliability on extremely large datasets Choice of several ML algorithms Amazon SageMaker: 10x better algorithms
  5. 5. © 2018, Amazon Web Services, Inc. or Its Affiliates. All rights reserved. Infinitely scalable algorithms
  6. 6. © 2018, Amazon Web Services, Inc. or its Affiliates. All rights reserved. GPU State Streaming
  7. 7. © 2018, Amazon Web Services, Inc. or its Affiliates. All rights reserved. Data Size Memory Data Size Time/Cost Streaming
  8. 8. © 2018, Amazon Web Services, Inc. or its Affiliates. All rights reserved. GPU State GPU State GPU State Distributed
  9. 9. © 2018, Amazon Web Services, Inc. or Its Affiliates. All rights reserved. Shared State GPU GPU GPU Local State Shared State Local State Local State
  10. 10. © 2018, Amazon Web Services, Inc. or its Affiliates. All rights reserved. Cost vs. Time $$$$ $$$ $$ $ Minutes Hours Days Weeks Months Best Alternative Amazon SageMaker
  11. 11. © 2018, Amazon Web Services, Inc. or its Affiliates. All rights reserved. Linear Learner Regression (mean squared error) SageMaker Other 1.02 1.06 1.09 1.02 0.332 0.183 0.086 0.129 83.3 84.5 Classification (F1 Score) SageMaker Other 0.980 0.981 0.870 0.930 0.997 0.997 0.978 0.964 0.914 0.859 0.470 0.472 0.903 0.908 0.508 0.508 30 GB datasets for web-spam and web-url classification 0 0.2 0.4 0.6 0.8 1 1.2 0 5 10 15 20 25 30 CostinDollars Billable time in Minutes sagemaker-url sagemaker-spam other-url other-spam
  12. 12. © 2018, Amazon Web Services, Inc. or its Affiliates. All rights reserved. 0 1 2 3 4 5 6 7 8 10 100 500 BillableTimeinMinutes Number of Clusters sagemaker other K-Means Clustering k SageMaker Other Text 1.2GB 10 1.18E3 1.18E3 100 1.00E3 9.77E2 500 9.18.E2 9.03E2 Images 9GB 10 3.29E2 3.28E2 100 2.72E2 2.71E2 500 2.17E2 Failed Videos 27GB 10 2.19E2 2.18E2 100 2.03E2 2.02E2 500 1.86E2 1.85E2 Advertising 127GB 10 1.72E7 Failed 100 1.30E7 Failed 500 1.03E7 Failed Synthetic 1100GB 10 3.81E7 Failed 100 3.51E7 Failed 500 2.81E7 Failed Running Time vs. Number of Clusters ~10x Faster!
  13. 13. © 2018, Amazon Web Services, Inc. or its Affiliates. All rights reserved. DeepAR: Time Series Forecasting Mean absolute percentage error P90 Loss DeepAR R DeepAR R traffic Hourly occupancy rate of 963 Bay Area freeways 0.14 0.27 0.13 0.24 electricity Electricity use of 370 homes over time 0.07 0.11 0.08 0.09 pageviews Page view hits of websites 10k 0.32 0.32 0.44 0.31 180k 0.32 0.34 0.29 NA One hour on p2.xlarge, $1 Input Network
  14. 14. © 2018, Amazon Web Services, Inc. or Its Affiliates. All rights reserved. Built in Algorithm Demo
  15. 15. © 2018, Amazon Web Services, Inc. or its Affiliates. All rights reserved. Image Classification • ResNet implementation with Apache MXNet. • More networks to come. • Transfer learning: begin with a model already trained on ImageNet! 0 0.5 1 1.5 2 2.5 3 3.5 0 1 2 3 4 5 Speedup Number of Machines (P2) Linear Speedup with Horizontal Scaling
  16. 16. © 2018, Amazon Web Services, Inc. or Its Affiliates. All rights reserved. Customer Stories
  17. 17. © 2018, Amazon Web Services, Inc. or its Affiliates. All rights reserved. Amazon SageMaker Customers
  18. 18. © 2018, Amazon Web Services, Inc. or its Affiliates. All rights reserved. Amazon SageMaker to automatically map buildings in Vietnam
  19. 19. © 2018, 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
  20. 20. © 2018, Amazon Web Services, Inc. or its Affiliates. All rights reserved. AWS DeepLens • Intel Atom Processor • Gen9 graphics • Ubuntu OS- 16.04 LTS • 100 GFLOPS performance • Dual band Wi-Fi • 8 GB RAM • 16 GB Storage (eMMC) • 32 GB SD card • 4 MP camera with MJPEG • H.264 encoding at 1080p resolution • 2 USB ports • Micro HDMI • Audio out • AWS Greengrass preconfigured • clDNN Optimized for MXNet
  21. 21. © 2018, Amazon Web Services, Inc. or its Affiliates. All rights reserved.
  22. 22. © 2018, 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 (free tier available) https://github.com/awslabs/amazon-sagemaker-examples An overview of Amazon SageMaker https://www.youtube.com/watch?v=ym7NEYEx9x4
  23. 23. © 2018, Amazon Web Services, Inc. or Its Affiliates. All rights reserved. Thank You

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