Amir sadoughi developing large-scale machine learning algorithms on amazon sage maker

1. © 2019, Amazon Web Services, Inc. or its affiliates. All rights reserved. Developing Large Scale Machine Learning Algorithms on Amazon SageMaker Amir Sadoughi Senior Software Engineer  Amazon AI Labs

2. © 2019, Amazon Web Services, Inc. or its affiliates. All rights reserved.© 2018, Amazon Web Services, Inc. or its Affiliates. All rights reserved. Amazon Confidential and 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 L E XR E K O G N I T I O N V I D E O Vision Speech Chatbots 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 Language Forecasting Recommendations T E X T R A C T New 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 New NewNew F O R E C A S T P E R S O N A L I Z E A M A Z O N S A G E M A K E R G R O U N D T R U T H New N O T E B O O K S A W S M A R K E T P L A C E New A L G O R I T H M S R E I N F O R C E M E N T   L E A R N I N G New T R A I N I N G O P T I M I Z A T I O N ( N E O ) New D E P L O Y M E N T H O S T I N G New

3. © 2019, Amazon Web Services, Inc. or its affiliates. All rights reserved. Machine Learning Amazon SageMaker Amazon SageMaker provides every developer and data scientist with the ability to build, train, and deploy machine learning models quickly.

5. © 2019, Amazon Web Services, Inc. or its affiliates. All rights reserved. Amazon SageMaker: Algorithms • Built-in algorithms • NLP • Computer Vision • Supervised • Unsupervised • AWS Marketplace for Machine Learning • Bring Your Own Algorithm

14. © 2019, Amazon Web Services, Inc. or its affiliates. All rights reserved. System design: Storage • Tiers: Amazon S3, Amazon EBS, GPU mem., CPU mem., CPU cache • Access patterns • Capacities: Throughput, Latency

15. © 2019, Amazon Web Services, Inc. or its affiliates. All rights reserved. System design: Storage File mode • easy to implement • faster for many epochs • initial download time • increased size for data disk space • maxes out at 16 TB Pipe mode • harder to implement • faster for single pass • downloads each epoch • sizing only for model disk space • no limit on length

17. © 2019, Amazon Web Services, Inc. or its affiliates. All rights reserved. System design: Network • Training: Single machine or distributed across many machines • Inference: number of concurrent requests, size of payload • Throughput • Latency • Jitter

20. © 2019, Amazon Web Services, Inc. or its affiliates. All rights reserved. Testing: benchmarking • Measure time, cost, accuracy • DAWNBench • Training: end-to-end throughput • Inference: end-to-end latency

22. © 2019, Amazon Web Services, Inc. or its affiliates. All rights reserved. System design: performance optimizations • Beware of the tradeoffs • Training • Low or mixed precision • Increase batch size • Optimize communication between workers

34. © 2019, Amazon Web Services, Inc. or its affiliates. All rights reserved. !34 Resources • SageMaker Product Page • SageMaker Console • Ground Truth Product Page • Neo Product Page • SageMaker RL Documentation • SageMaker 10-Minute Tutorial • SageMaker Related Blogs • Ground Truth Webinar (Dec 2018)

Amir sadoughi developing large-scale machine learning algorithms on amazon sage maker

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Amir sadoughi developing large-scale machine learning algorithms on amazon sage maker