5 antipatterns in scaling enterprise ai · 2019-11-22 · 5 antipatterns in scaling enterprise ai...
TRANSCRIPT
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5 Antipatterns in Scaling Enterprise AI Sarah King Director of Product, Molecula
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@ Molecula.
Background in Sales and Community
Management.
Cat Lady.
Krav Maga.
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At The Cusp Of The 5th Intelligence Era
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But, “AI” Is Falling Off The Hype Cliff Peak of Inflated
Expectations
Ex
pe
cta
tio
ns
Innovation
Trigger
Trough of
Disillusionment
Plateau of
Productivity
Time
● Data Not AI Ready
● Talent Shortage
● Data Moats Have Little Value
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What is an antipattern?
There must be at least two key elements present to formally distinguish an actual anti-pattern from a
simple bad habit, bad practice, or bad idea:
1. A commonly used process, structure, or pattern of action that despite initially appearing to be an
appropriate and effective response to a problem, has more bad consequences than good ones.
2. Another solution exists that is documented, repeatable, and proven to be effective.
Coined by Andrew Koenig and inspired by Design Patterns
https://en.wikipedia.org/wiki/Anti-pattern
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Many antipatterns in scaling Enterprise AI
1. Machine Learning projects happen in a
vacuum.
2. “Laptop Data Science”
3. All Data Scientists are seen as the same.
4. Putting Expensive Resources on Model
Management.
5. Not monitoring or communicating
outcomes.
Other Areas for Discussion: Bias, Data Privacy,
Feature Extraction and Reusability...
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1. Machine Learning projects happening in a vacuum.
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1. ML projects happening in a vacuum.
● People are protective of data in
their business units
● Silos are very difficult to navigate
● No collective interest or buy-in
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43% Of Enterprises lack a clear strategy for AI.
*McKinsey, ‘AI adoption advances, but foundational barriers remain,’ Nov ‘18
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1. Collaborate cross-functionally and communicate the initiative company-wide.
Data Science Marketing Sales Data Engineering
CRO CTO COO CDO
Prioritize Business Objectives and empower a cross functional team to execute together.
Tactical team collaborates on identifying Business Problems aligned with executive priority that can be solved by ML.
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2. “Laptop Data Science”
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2. “Laptop Data Science”
● Highly contested - big data sucks
to work with.
● Many times, working off of small
data is not representative of your
business’ production data.
● COPIES COPIES COPIES
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85% Of data in 2023 will be copies according to IDC.
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2. Big data is easy to work with.
● Access to 100% of data.
● Accessing data CAN be close to
real-time.
● Eliminate the back and forth that
can add days and weeks to your
project.
● Implement policies to protect
against aimless copying.
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Many solutions for working with big data
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3. All Data Scientists are seen as the same.
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3. All Data Scientists are seen as the same. Understand
Business Needs
Define MVP
Get Data
Data Prep
Train Models
Evaluate Models
Productionize
Models
Deploy Models
Make Predictions
Monitor
Predictions
Gather / Analyze
Insights 1. Define
2. Prototype
3. Production
4. Measure
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3. Take an inventory of skill sets on your team and hire/structure accordingly.
● Data Products need maintenance
that requires a broad range of skill
sets.
● Writing Algorithms
● Data Engineering
● Machine Learning Engineers/Ops
● DevOps
● Data Governance
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4. Putting the wrong people on production model management.
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4. Make models manage models.
● Automate this process
● Data Skewness Criterion
● Model Skewness Criterion
● Less expensive Dev resources can
then maintain
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5. Not monitoring or communicating outcomes.
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5. Not monitoring or communicating outcomes. It’s nice to look at shiny things!
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5. Monitor WITH and WITHOUT.
“Nearly every client looks at the deliverable vs. the outcome. It’s really easy to forget how critical it is to monitor a process with machine learning vs. without it.”
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Thank you! Sarah King Director of Product, Molecula [email protected] @sarahking_atx @molecula