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Build, Train, & Deploy ML Models Using SageMaker

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Amazon SageMaker is a fully-managed service that enables data scientists and developers to quickly and easily build, train, and deploy machine learning models, at scale. This session will introduce you the features of Amazon SageMaker, including a one-click training environment, highly-optimized machine learning algorithms with built-in model tuning, and deployment without engineering effort. With zero-setup required, Amazon SageMaker significantly decreases your training time and overall cost of building production machine learning systems.

Level: 200-300

Speaker: Randall Hunt - Sr. Technical Evangelist, AWS

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Build, Train, & Deploy ML Models Using SageMaker

  1. 1. © 2018, Amazon Web Services, Inc. or its Affiliates. All rights reserved. Pop-up Loft Build, train and deploy ML models using Amazon SageMaker
  2. 2. The Amazon Machine Learning Stack PLATFORM SERVICES APPLICATION SERVICES FRAMEWORKS & INTERFACES Caffe2 CNTK Apache MXNet PyTorch TensorFlow Chainer Keras Gluon AWS Deep Learning AMIs Amazon SageMaker Rekognition Transcribe Translate Polly Comprehend Lex AWS DeepLens EDUCATION Amazon Mechanical Turk
  3. 3. What is Amazon SageMaker?
  4. 4. Amazon SageMaker A fully-managed platform that provides the quickest and easiest way for data scientists and developers to get ML models from idea to production.
  5. 5. Amazon SageMaker components Amazon’s fast, scalable algorithms Distributed TensorFlow, Apache MXNet, Chainer, PyTorch Bring your own algorithm Hyperparameter Tuning Building HostingTraining
  6. 6. Amazon SageMaker components Amazon’s fast, scalable algorithms Distributed TensorFlow, Apache MXNet, Chainer, PyTorch Bring your own algorithm Hyperparameter Tuning Building HostingTraining
  7. 7. Building … or Apache Spark through EMR and the SageMaker Spark SDK... Use SageMaker‘s hosted Notebook Instances... ... or the Console for a point and click experience... ... or your own device (EC2, laptop, etc.)
  8. 8. Amazon SageMaker components Amazon’s fast, scalable algorithms Distributed TensorFlow, Apache MXNet, Chainer, PyTorch Bring your own algorithm Hyperparameter Tuning Building HostingTraining
  9. 9. Training Zero setup Streaming datasets + distributed compute Docker / ECS Deploy trained models locally or to Amazon SageMaker, AWS Greengrass, AWS DeepLens
  10. 10. Amazon SageMaker components Amazon’s fast, scalable algorithms Distributed TensorFlow, Apache MXNet, Chainer, PyTorch Bring your own algorithm Hyperparameter Tuning Building HostingTraining
  11. 11. Hosting One-click deployment Low latency, high throughput, and high reliability A/B testing Bring your own model
  12. 12. Amazon SageMaker components Amazon’s fast, scalable algorithms Distributed TensorFlow, Apache MXNet, Chainer, PyTorch Bring your own algorithm Hyperparameter Tuning Building HostingTraining
  13. 13. Built-in algorithms XGBoost, FM, Linear, and Forecasting for supervised learning Kmeans, PCA, and Word2Vec for clustering and pre- processing Image classification with convolutional neural networks LDA and NTM for topic modeling, seq2seq for translation
  14. 14. Amazon SageMaker components Amazon’s fast, scalable algorithms Distributed TensorFlow, Apache MXNet, Chainer, PyTorch Bring your own algorithm Hyperparameter Tuning Building HostingTraining
  15. 15. TensorFlow and Apache MXNet Docker Containers … explore and refine models in a single Notebook instance … deploy to production Sample your data… Use the same code to train on the full dataset in a cluster of instances…
  16. 16. Amazon SageMaker components Amazon’s fast, scalable algorithms Distributed TensorFlow, Apache MXNet, Chainer, PyTorch Bring your own algorithm Hyperparameter Tuning Building HostingTraining
  17. 17. Bring your own algorithm ... add algorithm code to a Docker container... Pick your preferred framework... ... publish to ECS Amazon ECS
  18. 18. Amazon SageMaker components Amazon’s fast, scalable algorithms Distributed TensorFlow, Apache MXNet, Chainer, PyTorch Bring your own algorithm Hyperparameter Tuning Building HostingTraining
  19. 19. Hyperparameter Tuning (Automated Model Tuning) Run a large set of training jobs with varying hyperparameters... ... and search the hyperparameter space for improved accuracy.
  20. 20. Zero setup for data exploration Resizable as you need Common tools pre-installed Easy access to your data sources No servers to manage
  21. 21. M o d u l a r a r c h i t e c t u r e Past Data Training algorithm Model artifacts Inference code Client application Model Data Inference Ground truth Amazon SageMaker
  22. 22. Pay as you go and inexpensive ML compute by the second starting at $0.0464/hr ML storage by the second at $0.14 per GB-month Data processed in notebooks and hosting at $0.016 per GB Free trial to get started quickly
  23. 23. Getting Started https://console.aws.amazon.com/sagemaker
  24. 24. Start with sample notebooks
  25. 25. Modify to access your data sources
  26. 26. Train your model
  27. 27. Deploy your model
  28. 28. Perform inferences
  29. 29. Demo: Neural networks and embeddings for recommendations
  30. 30. Demo: Bringing your own algorithm
  31. 31. Get Started today with Amazon SageMaker https://console.aws.amazon.com/sagemaker
  32. 32. Questions?
  33. 33. Thanks!

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