mit smr aws-2019-slidedeck-combined-rev2 · gold mine of data 18. #techatliberty most large...
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Gillian ArmstrongTechnologist with Liberty Mutual Insurance
Eric KesslerData scientist and practice manager for AI/ML at Amazon Web Services
John AshleyDirector, global financial services strategy, NVIDIA
Abbie LundbergBusiness technology analyst, Lundberg Media
Implementing AI: From Starting Out to Scaling Up
AI in the EnterpriseLearnings from Liberty Mutual’s Journey: A Practitioner’s PerspectiveGillian ArmstrongLiberty Mutual
#TechAtLiberty
Rolling out AI in an existing Enterprise
is Slow and Steady work
You can follow, but not fast
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#TechAtLiberty
You’ll need a Strategy
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� Education and Awareness� Assess your Existing Systems
− How capable are they of using or integrating with AI?− How comfortable is your company with the use of the Cloud?− Where and in what format is your Data?
� Assess your current in-house Skills− Where will you upskill your existing staff? When and how will you do that?− What will you need to hire for? At what point should you decide that?
� Assess Opportunities and Risks− Where will you start? How will you measure success?
#TechAtLiberty
A Strategy is “a plan of action designed to achieve a major or overall aim.”
Your assessments must lead to a plan of how and when you will prepare your
existing eco-system for the introduction of AI alongside “business as usual.”
This takes time.
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#TechAtLiberty 13
Build AND Buy
Pre-packaged solutionsMove Quick and Learn
Pay-as-you-go Cloud Services
#TechAtLiberty 14
Build AND Buy
Pre-packaged solutionsMove Quick and Learn
DifferentiateTotally custom solutions
Pay-as-you-go Cloud Services
#TechAtLiberty
Most large Enterprise companies are sitting on a
gold mine of data
But that’s the problem – it’s still in the mine!
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#TechAtLiberty
Some Questions to ask…
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� Where is your data and who can access it?� Do you have the right data for your problems?� Is your data in a format that can be used?
− Will it need to be pre-processed?− What is the data quality?− Does it cover all groups and/or scenarios? Could it have bias?− Are there any legal restrictions?− Are there any ethical concerns?
#TechAtLiberty
Some areas to consider…
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� Data Science� Machine Learning Development� Cloud Development� Business� User Experience� Legal / Contracts / Privacy / Security� PR / Marketing
© 2017, Amazon Web Services, Inc. or its Affiliates. All rights reserved.
What’s your company’s unfair advantage in ML?
Algorithms Talent Data
© 2017, Amazon Web Services, Inc. or its Affiliates. All rights reserved.
What’s your company’s unfair advantage in ML?
Algorithms Talent Data
© 2017, Amazon Web Services, Inc. or its Affiliates. All rights reserved.
ML presents organizational challenges
Inherently iterative and experimental
Dynamic business specifications
Non-standard testing
Feedback loops
Non-standard deployment patterns
© 2017, Amazon Web Services, Inc. or its Affiliates. All rights reserved.
ML using traditional IT org structures
Business unit Enterprise IT
Data Scientist
• Translates business problem to ML
• Explores & analyses data
• Builds model
Productionize• Captures requirements
and specifications• Develop pipeline and
deploys model• Operates and maintains
model• Manages change requests
© 2017, Amazon Web Services, Inc. or its Affiliates. All rights reserved.
A vertically integrated team for ML
Model
Pipeline
Product
Data Scientist
ML Engineer
Product Manager
ML
Flex team
UX/frontend engineer
Business SME
Cloud SMEs (Security, Networking, DevOps, etc.)
Core team
© 2017, Amazon Web Services, Inc. or its Affiliates. All rights reserved.
Accelerate adoption with an ML Center of Excellence
ML CoE
Business unit
Business unit
Provide full stack ML development to BUs across all functions (ML, Dev, Product)
Identify and develop ML roadmaps
Fight attrition through full pipeline of diverse ML projects
Initial adoption of ML
© 2017, Amazon Web Services, Inc. or its Affiliates. All rights reserved.
Accelerate adoption with an ML Center of Excellence
ML CoE
Business unit
Business unit
Supports decentralization through hiring and ML strategy
Build ML expertise in the BUs
Provide best practice guidance and support
Scaling ML inside of business units
© 2017, Amazon Web Services, Inc. or its Affiliates. All rights reserved.
Tenets of machine learning in production
Design for scalability, elasticity and stability
Treat data with same rigor as code
Enable full transparency and auditability
Automate as much as possible
Build for consistency by design