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Build, Train and Deploy ML Models using Amazon SageMaker
- 1. © 2018, AmazonWeb Services, Inc. or its Affiliates. All rights reserved. Hagay Lupesko, Amazon AI AWS ML Day in San Francisco, CA June 19 2018 Build, train and deploy ML models using Amazon SageMaker
- 2. © 2018, AmazonWeb Services, Inc. or its Affiliates. All rights reserved. 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 EMRAmazon MTurk
- 3. © 2018, AmazonWeb Services, Inc. or its Affiliates. All rights reserved. Amazon SageMaker A fully-managed platform that provides a quick and easy way to get models from idea to production. https://aws.amazon.com/sagemaker/
- 4. Amazon SageMaker Workflow Amazon’sfast, scalable algorithms Distributed TensorFlow, Apache MXNet, Chainer, PyTorch Bring your own algorithm Hyperparameter Tuning Building HostingTraining
- 5. Amazon SageMaker Workflow Amazon’sfast, scalable algorithms Distributed TensorFlow, Apache MXNet, Chainer, PyTorch Bring your own algorithm Hyperparameter Tuning Building HostingTraining
- 6. Building Machine LearningModels Create and manage cloud- based notebooks Use SageMaker‘s web interface to get started Or build models locally, then upload to SageMaker
- 7. Amazon SageMaker Workflow Amazon’sfast, scalable algorithms Distributed TensorFlow, Apache MXNet, Chainer, PyTorch Bring your own algorithm Hyperparameter Tuning Building HostingTraining
- 8. Training Machine LearningModels Zero setup Streaming datasets + distributed compute Deploy trained models to Amazon SageMaker, AWS Greengrass or AWS DeepLens Integrate with your Spark-based data pipeline
- 9. Amazon SageMaker Workflow Amazon’sfast, scalable algorithms Distributed TensorFlow, Apache MXNet, Chainer, PyTorch Bring your own algorithm Hyperparameter Tuning Building HostingTraining
- 10. Hosting One-click deployment Low latency, high throughput,and high reliability A/B testing Bring your own model
- 11. Amazon SageMaker Workflow Amazon’sfast, scalable algorithms Distributed TensorFlow, Apache MXNet, Chainer, PyTorch Bring your own algorithm Hyperparameter Tuning Building HostingTraining
- 12. SageMaker 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
- 13. Amazon SageMaker Workflow Amazon’sfast, scalable algorithms Distributed TensorFlow, Apache MXNet, Chainer, PyTorch Bring your own algorithm Hyperparameter Tuning Building HostingTraining
- 14. TensorFlow and ApacheMXNet 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…
- 15. Amazon SageMaker components Amazon’sfast, scalable algorithms Distributed TensorFlow, Apache MXNet, Chainer, PyTorch Bring your own algorithm Hyperparameter Tuning Building HostingTraining
- 16. Bring your ownalgorithm ... add algorithm code to a Docker container... Pick your preferred framework... ... publish your Docker image to ECR
- 17. Hyperparameter Tuning (Automated ModelTuning) Run a large set of training jobs with varying hyperparameters... ... and search the hyperparameter space for improved accuracy.
- 18. Zero setup fordata exploration Resizable as you need Common tools pre-installed Easy access to your data sources No servers to manage
- 19. Pay as yougo 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
- 20. © 2018, AmazonWeb Services, Inc. or its Affiliates. All rights reserved.© 2017, Amazon Web Services, Inc. or its Affiliates. All rights reserved. Demo Sentiment Analysis with SageMaker and MXNet https://bit.ly/2KEkBKt (source: https://github.com/lupesko/sentiment-analysis-with-sagemaker-mxnet)
- 21. © 2018, AmazonWeb Services, Inc. or its Affiliates. All rights reserved. Thank You To learn more, visit https://aws.amazon.com/sagemaker/
Editor's Notes
- #6 Let’s start with Building
- #22 So let's summarize what we went through: - We introduced SageMaker - We understood how you can use SM to build and traing your models - We learned how we can use SM to host your models - We went through a simple end to end example I will do another talk later today about model serving for deep learning for a more in-depth look into serving models. Hope you enjoyed!