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AWS Summit Singapore 2019 | Build, Train and Deploy Deep Learning Models on Amazon Sagemaker
The document discusses the capabilities and features of Amazon SageMaker for building, training, and deploying machine learning models. It emphasizes the importance of deep learning and neural networks, highlighting optimization challenges and the broad range of AWS machine learning services. Additionally, it presents AWS as a leading platform for popular frameworks like TensorFlow, PyTorch, and Apache MXNet.
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AWS Summit Singapore 2019 | Build, Train and Deploy Deep Learning Models on Amazon Sagemaker
- 1. © 2019, AmazonWeb Services, Inc. or its affiliates. All rights reserved.S U M M I T Build, train and deploy Deep Learning models on Amazon Sagemaker Pedro Paez Specialist Solutions Architect AWS
- 2. © 2019, AmazonWeb Services, Inc. or its affiliates. All rights reserved.S U M M I T Related breakouts Accelerating ML adoption with our new AI services Ben Snively Building Business outcome with Machine Learning on AWS Barnam Bora ML in the physical world with IoT Timothee Cruse
- 3. S U MM I T © 2019, Amazon Web Services, Inc. or its affiliates. All rights reserved.
- 4. © 2019, AmazonWeb Services, Inc. or its affiliates. All rights reserved.S U M M I T Machine Learning Y=f(X) X=input / Y=output Given many (X,Y) => Infer f
- 5. © 2019, AmazonWeb Services, Inc. or its affiliates. All rights reserved.S U M M I T Discriminating between classes Positive Negative Cats Dogs x x x x
- 6. © 2019, AmazonWeb Services, Inc. or its affiliates. All rights reserved.S U M M I T Making non-linear linear
- 7. © 2019, AmazonWeb Services, Inc. or its affiliates. All rights reserved.S U M M I T Biological learning Source: http://cs231n.github.io/neural-networks-1/
- 8. © 2019, AmazonWeb Services, Inc. or its affiliates. All rights reserved.S U M M I T Deep learning Hidden layers Input layer Output
- 9. © 2019, AmazonWeb Services, Inc. or its affiliates. All rights reserved.S U M M I T The “learning” in deep learning 0.4 0.3 0.2 0.9 ... ... backpropagation (gradient descent) !" != !" 0.4 ± # 0.3 ± # new weights new weights 0 1 0 1 1 . . . X input label ... !" ...
- 10. © 2019, AmazonWeb Services, Inc. or its affiliates. All rights reserved.S U M M I T General Concepts • A neural network with at least one hidden layer can approximate any function • The optimization is complicated and computationally very intensive due to non-convexity of the optimization space
- 11. S U MM I T © 2019, Amazon Web Services, Inc. or its affiliates. All rights reserved.
- 12. © 2019, AmazonWeb Services, Inc. or its affiliates. All rights reserved.S U M M I T © 2018, Amazon Web Services, Inc. or its Affiliates. All rights reserved. Amazon Trademark M L F R A M E W O R K S & I N F R A S T R U C T U R E The Amazon ML Stack: Broadest & Deepest Set of Capabilities A I S E R V I C E S R E K O G N I T I O N I M A G E P O L L Y T R A N S C R I B E T R A N S L A T E C O M P R E H E N D C O M P R E H E N D M E D I C A L L E XR E K O G N I T I O N V I D E O Vision Speech Chatbots A M A Z O N S A G E M A K E R B U I L D T R A I N F O R E C A S TT E X T R A C T P E R S O N A L I Z E D E P L O Y Pre-built algorithms & notebooks Data labeling (G R O U N D T R U T H ) One-click model training & tuning Optimization ( N E O ) One-click deployment & hosting M L S E R V I C E S F r a m e w o r k s I n t e r f a c e s I n f r a s t r u c t u r e E C 2 P 3 & P 3 d n E C 2 C 5 F P G A s G R E E N G R A S S E L A S T I C I N F E R E N C E Models without training data (REINFORCEMENT LEARNING)Algorithms & models ( A W S M A R K E T P L A C E ) Language Forecasting Recommendations NEW NEWNEW NEW NEW NEWNEW NEW NEW RL Coach
- 13. S U MM I T © 2019, Amazon Web Services, Inc. or its affiliates. All rights reserved.
- 14. © 2019, AmazonWeb Services, Inc. or its affiliates. All rights reserved.S U M M I T Amazon SageMaker: Build, Train, and Deploy ML Models at Scale 1 2 3
- 15. © 2019, AmazonWeb Services, Inc. or its affiliates. All rights reserved.S U M M I T Amazon SageMaker: Build, Train, and Deploy ML Models at Scale RL Coach
- 16. © 2019, AmazonWeb Services, Inc. or its affiliates. All rights reserved.S U M M I T Amazon SageMaker: Build, Train, and Deploy ML Models at Scale G R E E N G R A S SE C 2 C 5
- 17. © 2019, AmazonWeb Services, Inc. or its affiliates. All rights reserved.S U M M I T Amazon SageMaker: Build, Train, and Deploy ML Models at Scale
- 18. S U MM I T © 2019, Amazon Web Services, Inc. or its affiliates. All rights reserved. Pre-configured environments to quickly build deep learning applications
- 19. © 2019, AmazonWeb Services, Inc. or its affiliates. All rights reserved.S U M M I T AWS is framework agnostic Choose from popular frameworks Run them fully managed Or run them yourself
- 20. © 2019, AmazonWeb Services, Inc. or its affiliates. All rights reserved.S U M M I T The best place to run TensorFlow Fastest time for TensorFlow 65% 90% 30m 14m • 85% of TensorFlow workloads in the cloud runs on AWS (2018 Nucleus report) • Available w/ Amazon SageMaker and the AWS Deep Learning AMIs
- 21. © 2019, AmazonWeb Services, Inc. or its affiliates. All rights reserved.S U M M I T AWS: Best platform to run PyTorch Versatile PortableFlexible
- 22. © 2019, AmazonWeb Services, Inc. or its affiliates. All rights reserved.S U M M I T AWS is the best platform for Apache MXNet
- 23. S U MM I T © 2019, Amazon Web Services, Inc. or its affiliates. All rights reserved.
- 24. S U MM I T © 2019, Amazon Web Services, Inc. or its affiliates. All rights reserved. • Unpaired Image-to-Image Translation using Cycle-Consistent Adversarial Networks (UC Berkley): • https://junyanz.github.io/CycleGAN/ https://github.com/dmlc/gluon-cv https://github.com/pedrojpaez/summit_workshop
- 25. Thank you! S UM M I T © 2019, Amazon Web Services, Inc. or its affiliates. All rights reserved. Pedro Paez pppaez@amazon.com