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Kate Werling - Using Amazon SageMaker to build, train, and deploy your ML Models (200).pdf

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Amazon Web Services
Amazon Web Services

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.

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© 2018, Amazon Web Services, Inc. or its Affiliates. All rights reserved. Build, train and deploy ML models using Amazon SageMaker Kate Werling Solutions Architect
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
What is Amazon SageMaker?
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.
Amazon SageMaker components Amazon’s fast, scalable algorithms Distributed TensorFlow, Apache MXNet, Chainer, PyTorch Bring your own algorithm Hyperparameter Tuning Building HostingTraining
Amazon SageMaker components Amazon’s fast, scalable algorithms Distributed TensorFlow, Apache MXNet, Chainer, PyTorch Bring your own algorithm Hyperparameter Tuning Building HostingTraining
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.)
Amazon SageMaker components Amazon’s fast, scalable algorithms Distributed TensorFlow, Apache MXNet, Chainer, PyTorch Bring your own algorithm Hyperparameter Tuning Building HostingTraining
Training Zero setup Streaming datasets + distributed compute Docker / ECS Deploy trained models locally or to Amazon SageMaker, AWS Greengrass, AWS DeepLens
Amazon SageMaker components Amazon’s fast, scalable algorithms Distributed TensorFlow, Apache MXNet, Chainer, PyTorch Bring your own algorithm Hyperparameter Tuning Building HostingTraining
Hosting One-click deployment Low latency, high throughput, and high reliability A/B testing Bring your own model
Amazon SageMaker components Amazon’s fast, scalable algorithms Distributed TensorFlow, Apache MXNet, Chainer, PyTorch Bring your own algorithm Hyperparameter Tuning Building HostingTraining
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
Amazon SageMaker components Amazon’s fast, scalable algorithms Distributed TensorFlow, Apache MXNet, Chainer, PyTorch Bring your own algorithm Hyperparameter Tuning Building HostingTraining
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…
Amazon SageMaker components Amazon’s fast, scalable algorithms Distributed TensorFlow, Apache MXNet, Chainer, PyTorch Bring your own algorithm Hyperparameter Tuning Building HostingTraining
Bring your own algorithm ... add algorithm code to a Docker container... Pick your preferred framework... ... publish to ECS Amazon ECS
Amazon SageMaker components Amazon’s fast, scalable algorithms Distributed TensorFlow, Apache MXNet, Chainer, PyTorch Bring your own algorithm Hyperparameter Tuning Building HostingTraining
Hyperparameter Tuning (Automated Model Tuning) Run a large set of training jobs with varying hyperparameters... ... and search the hyperparameter space for improved accuracy.
Zero setup for data exploration Resizable as you need Common tools pre-installed Easy access to your data sources No servers to manage
M o d u lar a r chi te ctu re Past Data Training algorithm Model artifacts Inference code Client application Model Data Inference Ground truth Amazon SageMaker
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
Getting Started https://console.aws.amazon.com/sagemaker
Start with sample notebooks
Modify to access your data sources
Train your model
Deploy your model
Perform inferences
Demo: Neural networks and embeddings for recommendations
Demo: Bringing your own algorithm
Get Started today with Amazon SageMaker https://console.aws.amazon.com/sagemaker
Questions?
Thanks!

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Kate Werling - Using Amazon SageMaker to build, train, and deploy your ML Models (200).pdf

  • 1. © 2018, Amazon Web Services, Inc. or its Affiliates. All rights reserved. Build, train and deploy ML models using Amazon SageMaker Kate Werling Solutions Architect
  • 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. What is Amazon SageMaker?
  • 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. Amazon SageMaker components Amazon’s fast, scalable algorithms Distributed TensorFlow, Apache MXNet, Chainer, PyTorch Bring your own algorithm Hyperparameter Tuning Building HostingTraining
  • 6. Amazon SageMaker components Amazon’s fast, scalable algorithms Distributed TensorFlow, Apache MXNet, Chainer, PyTorch Bring your own algorithm Hyperparameter Tuning Building HostingTraining
  • 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. Amazon SageMaker components Amazon’s fast, scalable algorithms Distributed TensorFlow, Apache MXNet, Chainer, PyTorch Bring your own algorithm Hyperparameter Tuning Building HostingTraining
  • 9. Training Zero setup Streaming datasets + distributed compute Docker / ECS Deploy trained models locally or to Amazon SageMaker, AWS Greengrass, AWS DeepLens
  • 10. Amazon SageMaker components Amazon’s fast, scalable algorithms Distributed TensorFlow, Apache MXNet, Chainer, PyTorch Bring your own algorithm Hyperparameter Tuning Building HostingTraining
  • 11. Hosting One-click deployment Low latency, high throughput, and high reliability A/B testing Bring your own model
  • 12. Amazon SageMaker components Amazon’s fast, scalable algorithms Distributed TensorFlow, Apache MXNet, Chainer, PyTorch Bring your own algorithm Hyperparameter Tuning Building HostingTraining
  • 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. Amazon SageMaker components Amazon’s fast, scalable algorithms Distributed TensorFlow, Apache MXNet, Chainer, PyTorch Bring your own algorithm Hyperparameter Tuning Building HostingTraining
  • 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. Amazon SageMaker components Amazon’s fast, scalable algorithms Distributed TensorFlow, Apache MXNet, Chainer, PyTorch Bring your own algorithm Hyperparameter Tuning Building HostingTraining
  • 17. Bring your own algorithm ... add algorithm code to a Docker container... Pick your preferred framework... ... publish to ECS Amazon ECS
  • 18. Amazon SageMaker components Amazon’s fast, scalable algorithms Distributed TensorFlow, Apache MXNet, Chainer, PyTorch Bring your own algorithm Hyperparameter Tuning Building HostingTraining
  • 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. Zero setup for data exploration Resizable as you need Common tools pre-installed Easy access to your data sources No servers to manage
  • 21. M o d u lar a r chi te ctu re Past Data Training algorithm Model artifacts Inference code Client application Model Data Inference Ground truth Amazon SageMaker
  • 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
  • 24. Start with sample notebooks
  • 25. Modify to access your data sources
  • 29. Demo: Neural networks and embeddings for recommendations
  • 31. Get Started today with Amazon SageMaker https://console.aws.amazon.com/sagemaker