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AI & AWS DeepComposer

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AI & AWS DeepComposer

  1. 1. © 2020, Amazon Web Services, Inc. or its affiliates. All rights reserved. Artificial Intelligence & AWS DeepComposer Clifford Duke Solutions Architect S t a t e o f t h e U n i o n
  2. 2. The AWS ML Stack Broadest and most complete set of Machine Learning capabilities VISION SPEECH TEXT SEARCH CHATBOTS PERSONALIZATION FORECASTING FRAUD DEVELOPMENT CONTACT CENTERS Ground Truth Augmented AI ML Marketplace Neo Built-in algorithms Notebooks Experiments Model training & tuning Debugger Autopilot Model hosting Model Monitor Deep Learning AMIs & Containers GPUs & CPUs Elastic Inference Inferentia FPGA Amazon Rekognition Amazon Polly Amazon Transcribe +Medical Amazon Comprehend +Medical Amazon Translate Amazon Lex Amazon Personalize Amazon Forecast Amazon Fraud Detector Amazon CodeGuru AI SERVICES ML SERVICES ML FRAMEWORKS & INFRASTRUCTURE Amazon Textract Amazon Kendra Contact Lens For Amazon Connect SageMaker Studio IDE NEW NEW! NEW! NEW! NEW! NEW! NEW! NEW! NEW! NEW! NEW! Amazon SageMaker
  3. 3. The AWS ML Stack Broadest and most complete set of Machine Learning capabilities VISION SPEECH TEXT SEARCH CHATBOTS PERSONALIZATION FORECASTING FRAUD DEVELOPMENT CONTACT CENTERS Ground Truth Augmented AI ML Marketplace Neo Built-in algorithms Notebooks Experiments Model training & tuning Debugger Autopilot Model hosting Model Monitor Deep Learning AMIs & Containers GPUs & CPUs Elastic Inference Inferentia FPGA Amazon Rekognition Amazon Polly Amazon Transcribe +Medical Amazon Comprehend +Medical Amazon Translate Amazon Lex Amazon Personalize Amazon Forecast Amazon Fraud Detector Amazon CodeGuru AI SERVICES ML SERVICES ML FRAMEWORKS & INFRASTRUCTURE Amazon Textract Amazon Kendra Contact Lens For Amazon Connect SageMaker Studio IDE NEW NEW! NEW! NEW! NEW! NEW! NEW! NEW! NEW! NEW! NEW! Amazon SageMaker
  4. 4. © 2020, Amazon Web Services, Inc. or its affiliates. All rights reserved.
  5. 5. Amazon Rekognition: Deep-Learning-Based Image and Video Analysis
  6. 6. Video Amazon Rekognition applies machine learning to extract information from images and video Images
  7. 7. Amazon Rekognition Features Faces Celebrities Labels Moderation ScenesActivities Paths Text
  8. 8. © 2020, Amazon Web Services, Inc. or its affiliates. All rights reserved.
  9. 9. Rekognition custom labels
  10. 10. Rekognition custom labels
  11. 11. © 2020, Amazon Web Services, Inc. or its affiliates. All rights reserved.
  12. 12. Fraud comes in all shapes and forms Payment Fraud • Compromised Payment Instruments (e.g., stolen cards) • Intentional Non-Payment (e.g., pre-paid cards) Account Takeover/Compromise • Username/Password • API Key Abuse • Free Tier Misuse • Premium Phone Number
  13. 13. Fraud Prevention Strategy Prevention Detection Containment Remediation
  14. 14. Business Rules vs Machine Learning Business Rules look for specific conditions or behaviors • Business Rules are easily explained and validated • Sample New Account Registration rule: ML Models learn more general patterns by looking at lots of examples • When fraudsters make small tweaks, the model still recognizes them as suspicious since it’s unlike anything it has seen from legitimate customers • ML models are not just good at finding the risky patterns, they’re much less brittle than rules If IP_ADDRESS_LOCATION == [’Japan’] and CUST_ADDRESS_COUNTRY == [‘JAPAN’] and CUSTOMER_PHONE_LOC == [‘Spain’] THEN Investigate Prevention Detection
  15. 15. Fraud detection is difficult $$$ billions lost to fraud each year Online business prone to fraud attacks Bad actors change tactics often Rules = more human reviews Dependent on others to update detection logic
  16. 16. Fraud detection with ML is also difficult Top data scientists are costly & hard to find One-size-fits-all models underperform Often need to supplement data Data transformation + feature engineering Fraud imbalance = needle in a haystack
  17. 17. Introducing Amazon Fraud Detector A fraud detection service that makes it easy for businesses to use machine learning to detect online fraud in real-time, at scale.
  18. 18. Benefits of Amazon Fraud Detector • Build high quality fraud detection ML models faster • Stop bad actors at the door • Built-in online fraud expertise • Give fraud teams more control
  19. 19. Detect common types of online fraud Designed to help companies detect common types of online fraud Examples: • New account fraud • Online payment fraud (coming soon) • Guest checkout fraud • ‘Try Before You Buy’ + post-paid online service abuse
  20. 20. How it works
  21. 21. Automated model building 1 2 4 5 Training data in Amazon S3 63
  22. 22. Generating Fraud Predictions Guest Checkout: Purchase IP: 1.23.123.123 email: joe@example.com Payment: Bank123 … Fraud Detector returns: Outcome: Approved ML Score: 160 Purchase Approved Call service with: IP: 1.23.123.123 email: joe@example.com Payment: Bank123 … AWS Cloud
  23. 23. Key features Pre-built fraud detection model templates Automatic creation of custom fraud detection models Models learn from past attempts to defraud Amazon Amazon SageMaker integration Interface to review past events and detection logic
  24. 24. © 2020, Amazon Web Services, Inc. or its affiliates. All rights reserved.  Still a challenge today
  25. 25. Employees spend 20% of their time looking for information. —McKinsey 20% 44% 44% of the time, they cannot find the information they need to do their job. —IDC
  26. 26. Key Challenges • Low Accuracy • 80% of data is unstructured • Keyword Engines Complexity • Scattered Data Silos • Stale Search Results • Difficult to set up
  27. 27. Impact on Enterprise • Lower employee productivity • Increased risk and liability • Duplication of work • Creates negative customer experience
  28. 28. Impact on Enterprise $5,700 loss per employee per year* $114M lost per year for 20k employees
  29. 29. Introducing Amazon Kendra Highly accurate enterprise search service powered by machine learning.
  30. 30. Amazon Kendra-Rethinking Enterprise Search Easy to find what you are looking for Simple and quick to set up Native connectors Natural language Queries NLU and ML core Simple API and console experiences Code samples Continuous Improvement Domain Expertise
  31. 31. Amazon Kendra-Rethinking Enterprise Search Better Answers
  32. 32. Ask intuitive questions Natural language queries Keyword queries
  33. 33. Domain Expertise Optimized for 16 major domains More domains planned for 2020
  34. 34. Continuous Improvement-Incremental Learning Kendra improves automatically over time
  35. 35. Continuous Improvement-Relevance Tuning Prioritize your data  Fine Tune Kendra’s Accuracy
  36. 36. Kendra features—connectors …and more coming in 2020
  37. 37. Use cases Internal search supporting business functions such as operations, support, and R&D. External search helping your customers find the information they need. CRM, Content Management, and eDiscovery ISVs can build more intelligent and data-driven applications using Kendra.
  38. 38. © 2020, Amazon Web Services, Inc. or its affiliates. All rights reserved.
  39. 39. A day in the life of Lynn • Lynn is tech lead working on Java projects in an ecommerce company • part of a distributed development team • responsible for the backend services (search, order, and shipping) of her company’s high volume site • Her responsibilities span the entire application development and operations cycle • D: We found a data corruption issue in production. • L: Let’s find the root cause. D: I think it is due to a data race. Could we have caught it during code reviews? I wish we had someone who really understands concurrency. • O: The site latency is increasing. I just got paged! • L: Let’s find the root cause. O: The CPUs are overloaded. Can we increase the fleet size? • L: We increased the fleet size last month. The traffic is pretty much the same. What’s going on? • O: ??? • L: OK, let’s increase the fleet size. How do we find out what’s actually going on? I wish we’ve a performance expert in our team!
  40. 40. What’s on Lynn’s mind? How can we improve code quality? Are we giving lowest latency to our customers? Are our infrastructure costs just bloating?
  41. 41. Lynn’s ecosystem Write + Review Build + Test Deploy Measure Improve
  42. 42. What’s missing in Lynn’s ecosystem? • Detection of code defects early in the cycle • Keeping up with coding best practices • Identifying performance bottlenecks and linking them to code • Tools for visualizing application performance • Availability of expertise • Faster time to resolution and remediation • Developers need a truly integrated tool. • The tool should provide actionable recommendations across phases in the life cycle.
  43. 43. Introducing Amazon CodeGuru Automate code reviews Identify your most expensive lines of code
  44. 44. CodeGuru Amazon CodeGuru Reviewer Amazon CodeGuru Profiler
  45. 45. CodeGuru Reviewer • Using ML to detect code problems • Can connect to CodeCommit and GitHub
  46. 46. CodeGuru Profiler • Trained to find high-potential optimizations • High Latency • High CPU Utilization • Gives Code fix recommendations
  47. 47. Amazon Developer Feedback on Profiler Chris Butterfield, SDE CodeGuru Profiler’s recommended fix removed the thread contention which was using 55.97% of CPU time. After the fix a single host could now serve ~7.5x more traffic than before. We reduced our number of instances by ~75% while still handling the same traffic Rajesh Konatham, SDE After following Profiler’s recommendation to remove these clones, we saw huge reductions in CPU utilization – a 40% reduction on the synchronous fleet and 67% reduction on the asynchronous fleet
  48. 48. © 2020, Amazon Web Services, Inc. or its affiliates. All rights reserved.
  49. 49. Contact Lens for Amazon Connect Advanced search Detailed analytics & sentiment analysis Automated contact categorization Theme detection (coming soon) Supervisor assist (coming soon) Open and flexible data Contact Center Analytics for Amazon Connect powered by Machine Learning The out-of-the-box experience makes it easy for contact centers and their staff to use the power of ML with just a few clicks.
  50. 50. Enhanced Contact Trace Record
  51. 51. Automated Contact Categorization
  52. 52. Theme Detection
  53. 53. Real-time Supervisor analytics and alerting (mid- 2020)
  54. 54. The world’s first machine learning-enabled musical keyboard for developers
  55. 55. Autodesk - Airbus Autodesk – NASA JPL Gildewell Laboratories
  56. 56. Input a melody by connecting the AWS DeepComposer keyboard Choose from jazz, rock, pop, classical, or build your own custom genre model in Amazon SageMaker Publish your tracks to SoundCloud from the console. Export MIDI files to your favorite DAW
  57. 57. Thank you! © 2020, Amazon Web Services, Inc. or its affiliates. All rights reserved. Clifford Duke www.linkedin.com/in/cliffordghduke

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