AI is the buzzword while ML is the underlying component... but when do we use ML? To solve problems that machines can find patterns without explicitly programming them to do so. But do you have a team building an ML model? How far are they from the IT team? Do they know how to deploy and serve that? Testing? And sharing what they have done? That's where a devops mindset comes in: reduce the batch size, continuous-everything and a culture of failure/experimentation are vital for your data team! In the end, I will show how the workflow of a data scientist can be in real life with a live demo!

1. @thiagoavadore#DevOn
AI WITH A DEVOPS MINDSET
EXPERIMENTATION, SHARING & EASY ML DEPLOYMENT
THIAGO DE FARIA
HEAD OF SOLUTIONS ENGINEERING @LINKIT

2. @thiagoavadore#DevOn
SOURCING
SOLUTIONS
ENGINEERING
TRAINING

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~/
▸ Pure Math —> Software Engineer
▸ Trainer 🎤
▸ ❤ 👶 🎸 🎹 &
▸ OSS, 🐍, 🌧, Go, k8s, tensorflow, Scala & Spark
▸ Conference organiser - Amsterdam.ai, ITNEXT, …

4. @thiagoavadore#DevOn
GOOGLE AI MAKES PHONE CALLS??!!
CAN YOU DO SOMETHING LIKE THAT
USING THAT TENSORFLOW STUFF?
OUR USERS WOULD LOVE THAT!
PERSON A, VP OF PRODUCT
The real goal? A great PR

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WAIT… CAN YOU ALSO DO
THAT IN A CONTAINER USING
BLOCKCHAIN?
PERSON B, appointed new VP of product

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WHY DON’T WE CREATE AN AI DEVOPS
CLOUD-NATIVE APP ON K8S USING
BLOCKCHAIN BACKED BY IOT DEVICES THAT
THE USER WILL EXPERIENCE WITH A VR
HEADSET? A TRULY DISRUPTIVE DIGITAL
TRANSFORMATION!
PERSON C, appointed CEO and master-of-all things
Gets funded and moves to the Bay Area

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FIGHTING THE
DOWNWARDS SPIRAL
TIMELESS CONFLICT

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Product Dev IT Ops QA
Infosec

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

11. @thiagoavadore#DevOn
IF/ELSE, LOOPS &
FUNCTIONSNON-ML DEVELOPMENT

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DATA &
HYPERPARAMETERSML DEVELOPMENT

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DATA
PIPELINE
Manual
INGESTION
EXPLORE
CLEAN
FEATURE
ENGINEERING

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DATA
PIPELINE
MODEL DEV
EXPLORE
CLEAN
FEATURE
ENGINEERING
TRAIN
TESTTUNE
Manual
INGESTION

17. @thiagoavadore#DevOn
DATA
PIPELINE
MODEL DEV
EXPLORE
CLEAN
FEATURE
ENGINEERING
TRAIN
TESTTUNE
Automated
INGESTION

18. @thiagoavadore#DevOn
DATA
PIPELINE
MODEL DEVOPERATIONS
EXPLORE
CLEAN
FEATURE
ENGINEERING
TRAIN
TEST
TUNE
PACKAGE
LOGGING
MONITOR
DEPLOY
SERVE
Automated
INGESTION

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▸ Ethics issues
▸ Explainability problems
▸ Local Dev & Data —> Rework
▸ Hard to measure efficacy
▸ Nobody wants to be “on-call”

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SILVER BULLET
CULTURE CHANGE & NEW
WAYS OF WORKING.DOES IT SOUND SIMILAR?

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CHANGE THE CULTURE.
INVOLVE ML/DS.

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Product
Dev
IT Ops
QA
Infosec
ML Engineer
Data
Scientist

23. @thiagoavadore#DevOn
LOCAL DATA
SCIENCE IS DEAD

24. @thiagoavadore#DevOn
CI/CD FOR ML *IT ISN’T EASY…

25. @thiagoavadore#DevOn
MORE THAN CI/CD,
CONTINUOUS EVALUATION

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BIAS, DRIFT & FRAGILITY
ARE ML’S BUGS

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COLLABORATION.
VERSION - CODE & DATA.
EVALUATION.
PACKAGING.
DEPLOYMENT.
MODEL SERVING.

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TOOLS TO HELP…
▸ 2.3 on
▸…… on Ha. 3.1
▸.
▸.
▸aws-sagemaker-build

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DEMO GODS, BE KIND…

30. @thiagoavadore#DevOn
COLLABORATION
VERSION - CODE & DATA.
EVALUATION.
PACKAGING.
DEPLOYMENT.
MODEL SERVING.
!DEVOPS MOVEMENT!
!CULTURE CHANGE!
& MANY OTHERS

31. @thiagoavadore#DevOn
Questions?
SHAMELESS PLUG…

32. @thiagoavadore#DevOn
THE AMAZING PICTURES ARE FROM THE
GREAT JD HANCOCK!
PHOTOS.JDHANCOCK.COM