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© 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
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
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
© 2020, Amazon Web Services, Inc. or its affiliates. All rights reserved.
Amazon Rekognition:
Deep-Learning-Based Image and Video Analysis
Video
Amazon Rekognition applies machine learning
to extract information from images and video
Images
Amazon Rekognition Features
Faces Celebrities Labels
Moderation ScenesActivities Paths
Text
© 2020, Amazon Web Services, Inc. or its affiliates. All rights reserved.
Rekognition custom labels
Rekognition custom labels
© 2020, Amazon Web Services, Inc. or its affiliates. All rights reserved.
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
Fraud Prevention Strategy
Prevention
Detection
Containment
Remediation
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
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
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
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.
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
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
How it works
Automated model building
1 2 4 5
Training data in
Amazon S3
63
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
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
© 2020, Amazon Web Services, Inc. or its affiliates. All rights reserved.
 Still a challenge today
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
Key Challenges
• Low Accuracy
• 80% of data is unstructured
• Keyword Engines
Complexity
• Scattered Data Silos
• Stale Search Results
• Difficult to set up
Impact on Enterprise
• Lower employee productivity
• Increased risk and liability
• Duplication of work
• Creates negative customer experience
Impact on Enterprise
$5,700 loss per employee per year*
$114M lost per year
for 20k employees
Introducing Amazon Kendra
Highly accurate enterprise search service
powered by machine learning.
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
Amazon Kendra-Rethinking Enterprise Search
Better Answers
Ask intuitive questions
Natural language queries
Keyword queries
Domain Expertise
Optimized for 16 major domains
More domains planned for 2020
Continuous Improvement-Incremental Learning
Kendra improves
automatically over time
Continuous Improvement-Relevance Tuning
Prioritize your data
 Fine Tune Kendra’s Accuracy
Kendra features—connectors
…and more coming in 2020
AI & AWS DeepComposer
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.
© 2020, Amazon Web Services, Inc. or its affiliates. All rights reserved.
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!
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?
Lynn’s ecosystem
Write +
Review
Build +
Test
Deploy Measure Improve
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.
Introducing Amazon CodeGuru
Automate code reviews
Identify your most expensive lines of code
CodeGuru
Amazon CodeGuru
Reviewer
Amazon CodeGuru
Profiler
CodeGuru Reviewer
• Using ML to detect
code problems
• Can connect to
CodeCommit and
GitHub
CodeGuru Profiler
• Trained to find high-potential
optimizations
• High Latency
• High CPU Utilization
• Gives Code fix recommendations
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
© 2020, Amazon Web Services, Inc. or its affiliates. All rights reserved.
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.
Enhanced Contact
Trace Record
Automated Contact Categorization
Theme Detection
Real-time Supervisor analytics and alerting (mid-
2020)
The world’s first machine learning-enabled musical
keyboard for developers
Autodesk -
Airbus
Autodesk –
NASA JPL
Gildewell
Laboratories
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
Thank you!
© 2020, Amazon Web Services, Inc. or its affiliates. All rights reserved.
Clifford Duke
www.linkedin.com/in/cliffordghduke

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AI & AWS DeepComposer

  • 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. 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. 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. © 2020, Amazon Web Services, Inc. or its affiliates. All rights reserved.
  • 6. Video Amazon Rekognition applies machine learning to extract information from images and video Images
  • 7. Amazon Rekognition Features Faces Celebrities Labels Moderation ScenesActivities Paths Text
  • 8. © 2020, Amazon Web Services, Inc. or its affiliates. All rights reserved.
  • 11. © 2020, Amazon Web Services, Inc. or its affiliates. All rights reserved.
  • 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
  • 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. 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. 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. 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. 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. 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
  • 21. Automated model building 1 2 4 5 Training data in Amazon S3 63
  • 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. 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. © 2020, Amazon Web Services, Inc. or its affiliates. All rights reserved.  Still a challenge today
  • 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. Key Challenges • Low Accuracy • 80% of data is unstructured • Keyword Engines Complexity • Scattered Data Silos • Stale Search Results • Difficult to set up
  • 27. Impact on Enterprise • Lower employee productivity • Increased risk and liability • Duplication of work • Creates negative customer experience
  • 28. Impact on Enterprise $5,700 loss per employee per year* $114M lost per year for 20k employees
  • 29. Introducing Amazon Kendra Highly accurate enterprise search service powered by machine learning.
  • 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. Amazon Kendra-Rethinking Enterprise Search Better Answers
  • 32. Ask intuitive questions Natural language queries Keyword queries
  • 33. Domain Expertise Optimized for 16 major domains More domains planned for 2020
  • 34. Continuous Improvement-Incremental Learning Kendra improves automatically over time
  • 35. Continuous Improvement-Relevance Tuning Prioritize your data  Fine Tune Kendra’s Accuracy
  • 38. 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.
  • 39. © 2020, Amazon Web Services, Inc. or its affiliates. All rights reserved.
  • 40. 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!
  • 41. 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?
  • 42. Lynn’s ecosystem Write + Review Build + Test Deploy Measure Improve
  • 43. 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.
  • 44. Introducing Amazon CodeGuru Automate code reviews Identify your most expensive lines of code
  • 46. CodeGuru Reviewer • Using ML to detect code problems • Can connect to CodeCommit and GitHub
  • 47. CodeGuru Profiler • Trained to find high-potential optimizations • High Latency • High CPU Utilization • Gives Code fix recommendations
  • 48. 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
  • 49. © 2020, Amazon Web Services, Inc. or its affiliates. All rights reserved.
  • 50. 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.
  • 54. Real-time Supervisor analytics and alerting (mid- 2020)
  • 55. The world’s first machine learning-enabled musical keyboard for developers
  • 56. Autodesk - Airbus Autodesk – NASA JPL Gildewell Laboratories
  • 57. 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
  • 58. Thank you! © 2020, Amazon Web Services, Inc. or its affiliates. All rights reserved. Clifford Duke www.linkedin.com/in/cliffordghduke