AI is such a buzzword, with its futuristic implementations and sophisticated machine learning algorithms (Hello, Deep learning!). We are using ML when we need external data to reach a working product because it would be impossible to solve it with the regular for/if/loops. What are the next steps? Moreover, what about Test, Release and Deployment? We always value data and call our organizations “data-driven”, but now the impact is even bigger. If you are using a ML component, misused/dirty/problematic data will affect not your internal reports as before… but your application deployment and quality of service. Let’s hear discuss some AI implementations stories (its advantages/problems) finding common mistakes and future challenges for such a hyped theme.

1. #DevOpsDaysRiga @thiagoavadore
Thiago de Faria
DataOps & AI Lead
@LINKIT
Chaos while
deploying AI
and making
sure it
doesn’t hurt
your app

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THE TIMELESS
CONFLICT
IT Ops
Dev

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Product
Dev
IT Ops
QA
Infosec

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GOOGLE AI MAKES PHONE CALLS??!!
CAN YOU DO SOMETHING LIKE THAT
USING THAT TENSORFLOW STUFF?
IT WOULD REALLY HELP US GET MORE
FUNDING.
- PERSON A, VP OF PRODUCT, LAST WEEK

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WAIT… CAN YOU ALSO DO THAT IN A
CONTAINER USING BLOCKCHAIN?
- PERSON B, ONE DAY LATER
* PERSON B WAS APPOINTED VP OF PRODUCT

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SO OBVIOUS! WE WILL CREATE AN AI DEVOPS
CLOUD NATIVE APPLICATION ON KUBERNETES
USING BLOCKCHAIN THAT THE USER WILL
EXPERIENCE WITH A VR HEADSET AND THIS WILL
BE OUR DIGITAL TRANSFORMATION DISRUPTIVE
STRATEGY!
- PERSON C, YESTERDAY
* PERSON C IS THE NEW CEO

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AI
MAKE COMPUTERS
CAPABLE OF DOING
THINGS THAT WHEN
DONE BY A HUMAN,
WOULD BE THOUGHT
TO REQUIRE
INTELLIGENCE

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AI
MACHINE
LEARNING
MAKE MACHINES
FIND PATTERNS
WITHOUT EXPLICITLY
PROGRAMMING
THEM TO DO SO

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Product
Dev
IT Ops
QA
Infosec
Data Scientist
ML Engineer

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YOU DO
YOUR BEST
TO IMPROVE

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WHILE YOU
ROCK &
THROUGHPUT
INCREASES, A
DATA
SCIENTIST
BUILDS A
MODEL…

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LOCAL
DEVELOPMENT…

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CSV's ON A
LAPTOP..

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MODEL
BUILDING
TAKES TIME.

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SHOW TO
THE CEO!

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WE ARE AN AI
COMPANY!

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NOW DEPLOY
THIS!!!

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THE TIMELESS
CONFLICT
PART II - THE
RETURN OF AI
IT Ops
Dev
ML
Dev

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PROBLEMS I
HAVE SEEN
(some I may have
even caused…)

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DOING WHAT
YOU ARE NOT
GOOD &
HIRED FOR…

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FEATURE
ENGINEERING
WON’T EASILY
SCALE

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FROM CSV
TO EVENTS

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MODEL DRIFT
& FRAGILITY

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ONBOARDING

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HARD TO
MEASURE

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CULTURE
CHANGE

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CONTINUOUS
DELIVERY

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DEMO GODS,
be kind…
• Development visibility
• Code reusability
• Sharing
• Serving a model as REST
• Packaging the model
… more will come!

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The amazing pictures are from the great JD Hancock!
http://photos.jdhancock.com/

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Questions?