Artificial Intelligence is here this time, to stay. For the Enterprise, AI materializes into solutions that improve customers' experiences by optimizing, automating, and personalizing high-volume tasks while lowering cost and time to market, therefore accelerating innovation. In this session, we cover AWS' AI products and services that enable innovation in the enterprise while maintaining compliance with different regimes such as HIPAA, PCI, and more. Finally, we discuss enterprise architectures on AWS for machine learning and deep learning workloads.

1. @dmbanga @apwenchel© 2017, Amazon Web Services, Inc. or its Affiliates. All rights reserved. @dmbanga @apwenchel
Real-World AI For the Enterprise
D a n R . M b a n g a
B u s i n e s s D e v e l o p m e n t M a n a g e r
A I P l a t f o r m s a n d E n g i n e s
@dmbanga
E N T 3 0 1
N o v e m b e r 2 7 , 2 0 1 7
AWS re:Invent

2. @dmbanga @apwenchel© 2017, Amazon Web Services, Inc. or its Affiliates. All rights reserved.
A Cycle for AI
Perception

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A Cycle for AI
Perception
Learning
Knowledge
Representation
(KR)

4. @dmbanga @apwenchel© 2017, Amazon Web Services, Inc. or its Affiliates. All rights reserved.
A Cycle for AI
Perception
Learning
Knowledge
Representation
(KR)
Reasoning

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A Cycle for AI
Perception
Learning
Knowledge
Representation
(KR)
Reasoning
Planning
Execution

6. @dmbanga @apwenchel© 2017, Amazon Web Services, Inc. or its Affiliates. All rights reserved.
A Cycle for AI
Perception
Learning
Knowledge
Representation
(KR)
Reasoning
Planning
Execution
Vision
Language
Speech
Sensors

7. @dmbanga @apwenchel© 2017, Amazon Web Services, Inc. or its Affiliates. All rights reserved.
A Cycle for AI
Perception
Learning
Knowledge
Representation
(KR)
Reasoning
Planning
Execution
Vision
Language
Speech
Sensors
Brain (adaptive)
Memory (static)

8. @dmbanga @apwenchel© 2017, Amazon Web Services, Inc. or its Affiliates. All rights reserved.
A Cycle for AI
Perception
Learning
Knowledge
Representation
(KR)
Reasoning
Planning
Execution
Vision
Language
Speech
Sensors
Brain (adaptive)
Memory (static)
Actuators

9. @dmbanga @apwenchel© 2017, Amazon Web Services, Inc. or its Affiliates. All rights reserved.
A Cycle for AI
Perception
Learning
Knowledge
Representation
(KR)
Reasoning
Planning
Execution
Brain (adaptive)
Memory (static)
Actuators
Camera data
Audio data
Sensor data
Clicks
User activity

10. @dmbanga @apwenchel© 2017, Amazon Web Services, Inc. or its Affiliates. All rights reserved.
A Cycle for AI
Perception
Learning
Knowledge
Representation
(KR)
Reasoning
Planning
Execution
Brain (adaptive)
Actuators
Camera data
Audio data
Sensor data
Clicks
User activity
Knowledge Graph

11. @dmbanga @apwenchel© 2017, Amazon Web Services, Inc. or its Affiliates. All rights reserved.
A Cycle for AI
Perception
Learning
Knowledge
Representation
(KR)
Reasoning
Planning
Execution Actuators
Camera data
Audio data
Sensor data
Clicks
User activity
Knowledge Graph
Machine (Deep) Learning

12. @dmbanga @apwenchel© 2017, Amazon Web Services, Inc. or its Affiliates. All rights reserved.
A Cycle for AI
Perception
Learning
Knowledge
Representation
(KR)
Reasoning
Planning
Execution
Camera data
Audio data
Sensor data
Clicks
User activity
Machine (Deep) Learning
Knowledge Graph
AI

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The Promise of Artificial Intelligence:
INNOVATION

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Solving Some Of The Hardest Problems In Computer Science
Learning Language Perception Problem
Solving
Reasoning

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~1997

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18. @dmbanga @apwenchel© 2017, Amazon Web Services, Inc. or its Affiliates. All rights reserved.

19. @dmbanga @apwenchel© 2017, Amazon Web Services, Inc. or its Affiliates. All rights reserved.
• Applied Research
• Core Research
• Alexa
• Demand Forecasting
• Risk Analytics
• Search
• Recommendations
• AI Services
• Q&A Systems
• Supply Chain Optimization
• Advertising
• Machine Translation
• Video Content Analysis
• Robotics
• Lots of Computer Vision..
• NLP / NLU
Over 20 years of AI at Amazon…

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Detecting Politicians

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FDA Approved Medical Imaging

22. @dmbanga @apwenchel

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“How?”

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TP2-AI
Tools, People and Processes for AI

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Organizational Processes
How does machine learning fit in your organization?

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Data Visualization &
Analysis
Business Problem –
ML problem framing Data Collection
Data Integration
Data Preparation &
Cleaning
Feature Engineering
Model Training &
Parameter Tuning
Model Evaluation
Are business
goals met?
Model Deployment
Monitoring &
Debugging
– Predictions
YesNo
DataAugmentation
Feature
Augmentation
The Machine Learning Process
Re-training

27. @dmbanga @apwenchel© 2017, Amazon Web Services, Inc. or its Affiliates. All rights reserved.
Data Visualization &
Analysis
Business Problem –
ML problem framing Data Collection
Data Integration
Data Preparation &
Cleaning
Feature Engineering
Model Training &
Parameter Tuning
Model Evaluation
Are business
goals met?
Model Deployment
Monitoring &
Debugging
– Predictions
YesNo
DataAugmentation
Feature
Augmentation
Discovery: The Analysts
Re-training
• Help formulate the right
questions
• Domain Knowledge

28. @dmbanga @apwenchel© 2017, Amazon Web Services, Inc. or its Affiliates. All rights reserved.
Data Visualization &
Analysis
Business Problem –
ML problem framing Data Collection
Data Integration
Data Preparation &
Cleaning
Feature Engineering
Model Training &
Parameter Tuning
Model Evaluation
Are business
goals met?
Model Deployment
Monitoring &
Debugging
– Predictions
YesNo
DataAugmentation
Feature
Augmentation
Integration: The Data Architects
Re-training
• Build the data platform:
• Amazon S3
• AWS Glue
• Amazon Athena
• Amazon EMR
• Amazon Redshift
& Spectrum

29. @dmbanga @apwenchel© 2017, Amazon Web Services, Inc. or its Affiliates. All rights reserved.
Data Visualization &
Analysis
Business Problem –
ML problem framing Data Collection
Data Integration
Data Preparation &
Cleaning
Feature Engineering
Model Training &
Parameter Tuning
Model Evaluation
Are business
goals met?
Model Deployment
Monitoring &
Debugging
– Predictions
YesNo
DataAugmentation
Feature
Augmentation
Re-training
Modeling: The Data Scientists
• Builds the ML Models:
• Deep Learning AMI
• SparkML on EMR
• AI Services
• Amazon ML

30. @dmbanga @apwenchel© 2017, Amazon Web Services, Inc. or its Affiliates. All rights reserved.
Data Visualization &
Analysis
Business Problem –
ML problem framing Data Collection
Data Integration
Data Preparation &
Cleaning
Feature Engineering
Model Training &
Parameter Tuning
Model Evaluation
Are business
goals met?
Model Deployment
Monitoring &
Debugging
– Predictions
YesNo
DataAugmentation
Feature
Augmentation
Re-training
Production: The SDE and the DevOps
• Build Smart Apps
• AWS Lambda
• Amazon S3
• Amazon API Gateway
• AWS IoT
• Amazon Kinesis
• Amazon ECS/ECR
• Mobile Hub
• AWS KMS
• Amazon EC2
• More…

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Security & Encryption Integrated Across The Platform
Fine grained
access controls
Broad KMS
integration
Server-side
encryption
with CMK
Audit key
usage by
user & role
Import
keys
Policy
validation
& simulation

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Key AWS Certifications and Assurance Programs

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Intelligent Services Powered By Deep Learning
Amazon AI

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More in
2017
Infrastructure CPU
Engines MXNet TensorFlow Caffe Theano Pytorch CNTK
Services
Amazon Polly
Platforms
IoT
Speech
Mobile
Amazon
ML
Spark &
EMR
Kinesis Batch ECS
GPU
More in
2017
Chat
Amazon LexAmazon Rekognition
Vision
Amazon AI: Machine Learning In The Hands Of Every Developer

35. @dmbanga @apwenchel© 2017, Amazon Web Services, Inc. or its Affiliates. All rights reserved.
Machine Learning in the Enterprise
…but how?

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Adam Wenchel, VP of AI

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“The future is already here,
it’s just not very evenly distributed.”
—William Gibson

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39. @dmbanga @apwenchel© 2017, Amazon Web Services, Inc. or its Affiliates. All rights reserved.
Remember the infrastructure Built on AWS ML takes time, and technical debt
Democratize AI, responsibly Maximize scarce experts’ productivity There is a lot more than the ML model
Machine Learning @ Capital One

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The road to production is long and arduous
Prepare for the journey

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Model selection and Hyper-
parameter Tuning
GPU Optimization Self-service
Production is a hard place Continuous Monitoring Rapid ML model refit/deploy
Capital One AI: The Road to Production

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Machine Learning Technology @ Capital One
Built on AWS

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Amazon AI AI Platforms AI Engines & InfraAI Services
Capital One AI: Democratizing ML in a Well-Managed Way
Experiment
Management
Logging Versioning Reproducibility Monitoring
Optimize &
Scale
Model Optimization GPU saturation
Data/Compute
coordination
CapitalOne

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Organizing for Success
You want a Data Science Team, then what ?

45. @dmbanga @apwenchel© 2017, Amazon Web Services, Inc. or its Affiliates. All rights reserved.
People
Machine Learning is inherently Interdisciplinary
Physical
Scientists
Analysts
Architects/
Integrators
Computer
Scientists

46. @dmbanga @apwenchel© 2017, Amazon Web Services, Inc. or its Affiliates. All rights reserved.
People
Physical
Scientists
Analysts
Architects/
Integrators
Computer
Scientists
Used to solving
problems by
applying ML to
sensors data
Machine Learning is inherently Interdisciplinary

47. @dmbanga @apwenchel© 2017, Amazon Web Services, Inc. or its Affiliates. All rights reserved.
People
Machine Learning is inherently Interdisciplinary
Physical
Scientists
Analysts
Architects/
Integrators
Computer
Scientists
Used to solving
problems by
applying ML to
sensors data
Understand
Business
Problem
Framing

48. @dmbanga @apwenchel© 2017, Amazon Web Services, Inc. or its Affiliates. All rights reserved.
People
Machine Learning is inherently Interdisciplinary
Physical
Scientists
Analysts
Architects/
Integrators
Computer
Scientists
Used to solving
problems by
applying ML to
sensors data
Understand
Business
Problem
Framing
Understand
Computer
Logic and ML
Science

49. @dmbanga @apwenchel© 2017, Amazon Web Services, Inc. or its Affiliates. All rights reserved.
People
Machine Learning is inherently Interdisciplinary
Physical
Scientists
Analysts
Architects/
Integrators
Computer
Scientists
Used to solving
problems by
applying ML to
sensors data
Understand
Business
Problem
Framing
Understand
Computer
Logic and ML
Science
Can put
everything
together

50. @dmbanga @apwenchel© 2017, Amazon Web Services, Inc. or its Affiliates. All rights reserved.
Capital One AI: How We Organize for Success
Create a positive work environment for
ML talent
Centralize new technologiesAttract and motivate the right talent

51. @dmbanga @apwenchel© 2017, Amazon Web Services, Inc. or its Affiliates. All rights reserved.

52. @dmbanga @apwenchel© 2017, Amazon Web Services, Inc. or its Affiliates. All rights reserved.
Opportunity: Transform Financial Services
Increase efficiency, creating value for our
customers
New ways of empowering customers
to take control of their financial lives
Create amazing customer experiences

53. @dmbanga @apwenchel© 2017, Amazon Web Services, Inc. or its Affiliates. All rights reserved.

54. The Deep Learning Revolution
Terrence Sejnowski, The Salk Institute for Biological Studies
Eye, Robot: Computer Vision and Autonomous
Robotics
Aaron Ames & Pietro Perona, California Institute of Technology
Exploiting the Power of Language
Alexander Smola, Amazon Web Services
Reducing Supervision: Making More with Less
Martial Hebert, Carnegie Mellon University
Learning Where to Look in Video
Kristen Grauman, University of Texas
Look, Listen, Learn: The Intersection of Vision and
Sound
Antonio Torralba, MIT
Investing in the Deep Learning Future
Matt Ocko, Data Collective Venture Capital
Thursday, November 30th
1:00 - 5:00pm | Venetian, Ballroom F
https://reinvent.awsevents.com/learn/deep-learning-summit/

55. @dmbanga @apwenchel© 2017, Amazon Web Services, Inc. or its Affiliates. All rights reserved. @dmbanga @apwenchel
Thank you!
d m m b a n g a @ a m a z o n . c o m