DevOps for data science: automate the boring stuff and leverage the OSS ecosystem

Transcript

1. DevOps for Data
   Science?
   Automate the boring stuff and leverage the
   OSS ecosystem
   PyCon Africa – August 6th, 2020
   Tania Allard, PhD @ixek
   

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2. @ixek
   @trallard
   trallard.dev
   

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3. About Me
   I Python
   I am also a GDE for Tensorflow
   I love mechanical keyboards
   My dog usually barks while I
   am giving online talks
   

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4. These slides
   https://bit.ly/mlops-pyconafrica
   

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5. background
   ML and Data Science
   in 2020
   Table of
   Contents
   1
   What is even
   MLOps?
   And why you’d need it…
   2
   Mlops 101
   Getting started with
   MLOps
   3
   @ixek
   

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6. background
   ML and Data Science in 2020
   01
   

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7. Where
   have we
   been?
   The Garner hype cycle @ixek
   

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8. Data Scientist
   It’s never been easier
   to run ML experiments
   ML engineer
   /SRE
   Machine learning in
   production is hard y’all!
   Every team
   @ixek
   

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9. ● Tools like scikit-learn and Keras make it easy to
   create models in a few lines
   ● Techniques like transfer learning make our lifes easier
   ● More Compute! All the GPUs!
   From the DS
   perspective
   

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10. The new unicorn
    Must have
    Analytical skills
    Software eng.
    Programming
    Data engineering
    Data visualization
    Also must have
    Containerization
    End-to-end ML pipeline
    CI /CD /Versioning
    Deep learning / NLP / etc.
    Privacy and security
    @ixek
    

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11. MLOps
    What is it?
    02
    

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12. Where is my
    unicorn?
    A mythical data
    scientist who can code,
    write unit tests AND
    resist the lure of a
    deep neural network
    when logistic
    regression will do.
    

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13. The origin of devops
    Software developers:
    Need to move and iterate fast
    Operation team:
    Stability and availability of
    services is priority
    @ixek
    

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14. DevOps is the union of
    people, process, and
    products to enable
    continuous delivery of value
    into production
    - Donovan Brown
    “
    @ixek
    

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15. Automate
    Automate
    everything you can
    (data processing,
    model training)
    Feedback
    Get feedback on
    new ideas fast (test
    immediately)
    No manual
    handoffs
    Provide early
    testing
    opportunities
    DevOps principles
    @ixek
    

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16. Continuous integration – software engineering
    Based on test
    results – no
    waiting time*
    Quick testing
    Automated build
    Project source
    code in version
    control
    Code
    changes
    Automate Feedback iterate
    @ixek
    

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17. Technical
    considerations
    ● Reliance on metrics
    (e.g. accuracy, specifity)
    ● Data visualization
    ● Required domain
    knowledge
    So what
    about ML?
    @ixek
    

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18. More than ML code / model
    @ixek
    

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19. The origin of mlops
    Data scientist:
    • Need to move and
    iterate fast
    • Use my loved
    frameworks
    • Scalable
    • Minimal wait: test, stage
    production
    SRE/ML Engineers:
    • Reuse of tooling and
    platforms
    • Uptime
    • Monitoring
    • Reliability and stability
    @ixek
    

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20. Continuous integration – software engineering
    Improve model
    based on
    outputs/outcomes
    Sought metrics
    Automated
    training / data
    processing
    Project source
    code in version
    control. Data
    lineage.
    Code& data
    changes
    Automate Feedback iterate
    @ixek
    

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21. Getting started
    101 MLOps
    03
    

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22. RECYCLE YOUR ECOSYSTEM
    1 Collaboration
    Version control (Git, Mercurial)
    OSS dev platform / CI /CD
    (GitHub, GitLab, Travis)
    2 automation Leverage your deployment
    infrastructure (CI / CD, Make)
    3 Mix-match Use the OSS libraries you love and
    leverage cloud computing*
    @ixek
    

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23. MlOps step by step
    ENV
    #1
    CI/CD Pipeline
    Process Train Stage Serve
    Data
    Distributed Cloud
    ENV
    #2
    Data Scientist
    SRE/ML Engineers
    @ixek
    

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24. CI/CD Pipeline
    Process Train Stage Serve
    Data
    Distributed Cloud
    First, I check in
    my code.
    ENV
    #1
    ENV
    #2
    Data Scientist
    Data Scientist
    SRE/ML Engineers
    MlOps step by step
    @ixek
    

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25. Version
    control
    @ixek
    

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26. CI/CD Pipeline
    Process Train Stage Serve
    Data
    Distributed Cloud
    That kicks off
    a CI/CD Pipeline.
    ENV
    #1
    ENV
    #2
    Data Scientist
    SRE/ML Engineers
    MlOps step by step
    @ixek
    

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27. Kicking CI
    /CD
    Push changes
    GitHub actions
    @ixek
    

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28. CI/CD Pipeline
    Process Train Stage Serve
    Data
    Distributed Cloud
    And now do a
    training run on the
    processed data
    ENV
    #1
    ENV
    #2
    Data Scientist
    SRE/ML Engineers
    MlOps step by step
    @ixek
    

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29. Not only tests
    Can leverage to do the
    training or data
    processing
    Vision
    Venus has a beautiful
    name and is the second
    planet from the Sun. It’s
    atmosphere is extremely
    poisonous
    @ixek
    

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30. CI/CD Pipeline
    Process Train Stage Serve
    Data
    Distributed Cloud
    Actually need to
    update the
    parameters
    ENV
    #1
    ENV
    #2
    Data Scientist
    SRE/ML Engineers
    MlOps step by step
    

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31. Parameters update?
    No problem check in to
    version control
    @ixek
    

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32. Updated reporting
    Embed reports and
    metrics to your Pull
    request
    @ixek
    

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33. Updated reporting
    Embed reports and
    metrics to your Pull
    request
    @ixek
    

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34. CI/CD Pipeline
    Process Train Stage Serve
    Data
    Distributed Cloud
    Model is optimized
    and working! Let’s
    roll out to
    production.
    ENV
    #1
    ENV
    #2
    Data Scientist
    SRE/ML Engineers
    MlOps step by step
    @ixek
    

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35. CI/CD Pipeline
    Process Train Stage Serve
    Data
    Distributed Cloud
    Trigger the CI/CD
    pipeline one last
    time.
    ENV
    #1
    ENV
    #2
    Data Scientist
    SRE/ML Engineers
    MlOps step by step
    @ixek
    

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36. CI/CD Pipeline
    Process Train Stage Serve
    Data
    Distributed Cloud
    And roll out
    to the world!
    ENV
    #1
    ENV
    #2
    Data Scientist
    SRE/ML Engineers
    MlOps step by step
    @ixek
    

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37. But there
    is more
    @ixek
    

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38. But there
    is more
    @ixek
    

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39. But there
    is more
    @ixek
    

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40. In brief
    MLOps allows you to be more efficient with the
    tools you use and love
    @ixek
    

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41. RECYCLE YOUR ECOSYSTEM
    1 Collaboration
    Version control (Git, Mercurial)
    OSS dev platform / CI /CD
    (GitHub, GitLab, Travis)
    2 automation Leverage your deployment
    infrastructure (CI / CD, Make)
    3 Mix-match Use the OSS libraries you love and
    leverage cloud computing*
    @ixek
    

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42. These slides
    https://bit.ly/mlops-pyconafrica
    

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43. Thanks!
    @ixek
    @trallard
    trallard.dev
    

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