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AWS Summit Singapore 2019 | Building Business Outcomes with Machine Learning on AWS

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Speaker: Barnam Bora, Head of AI/ML, APAC, AWS

Customer Speaker: Guangda Li, Co-founder & CTO, ViSenze
AWS offers different paths for building and deploying scalable ML solutions. This session provides an insight to how AWS customers are building intelligent systems powered by AI and ML. Learn how these services, in conjunction with the large number of complementary AWS technologies, provide a great platform for our customers to build their own AI and ML powered solutions and drive business value. Towards the latter part of this session, hear how customers are deploying their ML on AWS and can now leverage Marketplace to monetise their models.

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AWS Summit Singapore 2019 | Building Business Outcomes with Machine Learning on AWS

  1. 1. © 2019, Amazon Web Services, Inc. or its affiliates. All rights reserved.S U M M I T Building Business Outcomes with AI & Machine Learning on AWS Barnam Bora Head of AI & Machine Learning – Asia-Pacific Amazon Web Services Guangda Li Co-founder & CTO ViSenze
  2. 2. © 2019, Amazon Web Services, Inc. or its affiliates. All rights reserved.S U M M I T
  3. 3. © 2019, Amazon Web Services, Inc. or its affiliates. All rights reserved.S U M M I T AI is the New Normal
  4. 4. © 2019, Amazon Web Services, Inc. or its affiliates. All rights reserved.S U M M I T AI is the New Normal – Why? 2018 WW GDP $ 80 Tn
  5. 5. © 2019, Amazon Web Services, Inc. or its affiliates. All rights reserved.S U M M I T 2030 WW GDP *projected $ 112 Tn -Sizing the prize PwC’s Global Artificial Intelligence Study: Exploiting the AI Revolution 27 June 2017 2018 WW GDP $ 80 Tn AI is the New Normal – Why?
  6. 6. © 2019, Amazon Web Services, Inc. or its affiliates. All rights reserved.S U M M I T AI is the New Normal – Why? 2030 WW GDP *projected $ 112 Tn $ 15.7 Tn AI -Sizing the prize PwC’s Global Artificial Intelligence Study: Exploiting the AI Revolution 27 June 2017 2018 WW GDP $ 80 Tn -Sizing the prize PwC’s Global Artificial Intelligence Study: Exploiting the AI Revolution 27 June 2017
  7. 7. © 2019, Amazon Web Services, Inc. or its affiliates. All rights reserved.S U M M I T AI is the New Normal – Why? 2030 WW GDP *projected $ 112 Tn $ 15.7 Tn AI The BUILDERS who embrace AI will be the engines of human acceleration. 14% OF Human Productivity in 2030 2018 WW GDP $ 80 Tn -Sizing the prize PwC’s Global Artificial Intelligence Study: Exploiting the AI Revolution 27 June 2017
  8. 8. © 2019, Amazon Web Services, Inc. or its affiliates. All rights reserved.S U M M I T Defining AI - Popularly When a machine mimics "cognitive" functions that we associate with other humans, such as "learning" and "problem solving".
  9. 9. © 2019, Amazon Web Services, Inc. or its affiliates. All rights reserved.S U M M I T Defining AI – Computer Science The study of "intelligent agents": i.e. any human-made agent that perceives its environment and takes actions that maximize its chances of success at some defined goal.
  10. 10. © 2019, Amazon Web Services, Inc. or its affiliates. All rights reserved.S U M M I T Data Science vs AI vs Machine Learning vs Deep Learning Deep Learning Machine Learning Data Science Artificial Intelligence
  11. 11. © 2019, Amazon Web Services, Inc. or its affiliates. All rights reserved.S U M M I T Defining Machine Learning for Business Outcomes Relevant Data Domain Expertise Intelligent Business Systems & Experiments Automate - Repeated Decisions Assist - Human Decisions Prescribe - Process Improvements Disrupt - Build Net-New Outcomes B U S I N E S S V A L U E Predict - Future Behavior
  12. 12. © 2019, Amazon Web Services, Inc. or its affiliates. All rights reserved.S U M M I T
  13. 13. © 2019, Amazon Web Services, Inc. or its affiliates. All rights reserved.S U M M I T
  14. 14. © 2019, Amazon Web Services, Inc. or its affiliates. All rights reserved.S U M M I T
  15. 15. © 2019, Amazon Web Services, Inc. or its affiliates. All rights reserved.S U M M I T
  16. 16. © 2019, Amazon Web Services, Inc. or its affiliates. All rights reserved.S U M M I T
  17. 17. © 2019, Amazon Web Services, Inc. or its affiliates. All rights reserved.S U M M I T Physics Chemistry Math Civil Electives Physics Chemistry Math Mechanical Engineer Civil Engineer Transfer Learning Mechanical Electives
  18. 18. © 2019, Amazon Web Services, Inc. or its affiliates. All rights reserved.S U M M I T k-means Clustering k-Nearest Neighbour (k-NN) PCA Neural Topic Modelling Factorisation Machines Linear Learner – Regression XGBoost LDA Image Classification Object Detection Seq2Seq Linear Learner Binary Classification DeepAR Forecasting ML Prediction now becomes a RESTful API call Amazon Sagemaker: Built-In Algorithms
  19. 19. © 2019, Amazon Web Services, Inc. or its affiliates. All rights reserved.S U M M I T Our Mission in AWS is to put Machine Learning in the hands of every Developer
  20. 20. © 2019, Amazon Web Services, Inc. or its affiliates. All rights reserved.S U M M I T Few Years Ago
  21. 21. © 2019, Amazon Web Services, Inc. or its affiliates. All rights reserved.S U M M I T 2 Years Ago
  22. 22. © 2019, Amazon Web Services, Inc. or its affiliates. All rights reserved.S U M M I T
  23. 23. © 2019, Amazon Web Services, Inc. or its affiliates. All rights reserved.S U M M I T Every day we apply new AI/ML based improvements to the Amazon business, at a global scale through AWS
  24. 24. © 2019, Amazon Web Services, Inc. or its affiliates. All rights reserved.S U M M I T Amazon Scout, a Machine Learning powered visual shopping tool for personalized Recommendations …. www.amazon.com/scout
  25. 25. © 2019, Amazon Web Services, Inc. or its affiliates. All rights reserved.S U M M I T Challenges that have so-far prevented organizations from adopting Machine Learning quickly? ML expertise is rare Building and scaling ML technology is hard Deploying and operating models in production is time- consuming and expensive A lack of cost- effective, easy-to-use, and scalable ML services
  26. 26. © 2019, Amazon Web Services, Inc. or its affiliates. All rights reserved.S U M M I T Typical ML Workflow – Customer Pain Points Data Acquisition & Storage1 Model & Framework selection3 Model Training4 Hyper parameter tuning5 Model testing and simulation6 Model Deployment (inference)7 Data Labelling2 • Iterative and time consuming. • Image recognition/ object detection require training with over a million images with each image containing over 65,000 pixels. • Speech recognition and synthesis require training with 100s of hours of human speech. Training 26
  27. 27. © 2019, Amazon Web Services, Inc. or its affiliates. All rights reserved.S U M M I T Customers want to Train & Run models in the cloud at scale Customers want to Run models at the edge AWS IoT Platform Tesla V100 120 TFLOPS AWS Greengrass Amazon SageMaker Amazon EC2
  28. 28. © 2019, Amazon Web Services, Inc. or its affiliates. All rights reserved.S U M M I T We set out to build a Machine Learning platform that is accessible to every Developer, Data Scientist & IT Professional
  29. 29. © 2019, Amazon Web Services, Inc. or its affiliates. All rights reserved.S U M M I T Customers need a complete end-to-end Machine Learning stack 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 A I S E R V I C E S M L S E R V I C E S 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 A L G O R I T H M S N O T E B O O K S M A R K E T P L A C E U N S U P E R V I S E D L E A R N I N G S U P E R V I S E D L E A R N I N G R E I N F O R C E M E N T L E A R N I N G O P T I M I Z A T I O N ( N E O ) T R A I N I N G H O S T I N G D E P L O Y M E N T Frameworks Interfaces Infrastructure 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 L E XR E K O G N I T I O N V I D E O Vision Speech Language Chat-bots Forecasting Recommendations T E X T R A C T F O R E C A S T P E R S O N A L I Z 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 I N F E R E N T I A
  30. 30. © 2019, Amazon Web Services, Inc. or its affiliates. All rights reserved.S U M M I T Customers need a complete end-to-end Machine Learning stack 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 A I S E R V I C E S M L S E R V I C E S 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 A L G O R I T H M S N O T E B O O K S M A R K E T P L A C E U N S U P E R V I S E D L E A R N I N G S U P E R V I S E D L E A R N I N G R E I N F O R C E M E N T L E A R N I N G O P T I M I Z A T I O N ( N E O ) T R A I N I N G H O S T I N G D E P L O Y M E N T Frameworks Interfaces Infrastructure 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 L E XR E K O G N I T I O N V I D E O Vision Speech Language Chat-bots Forecasting Recommendations T E X T R A C T F O R E C A S T P E R S O N A L I Z 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 I N F E R E N T I A

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