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Tania Allard, PhD @ixek Developer Advocate @Microsoft Practical DevOps for the busy Data Scientist http://bit.ly/MancML-trallard

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2 A bit of background never hurt anyone About us

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3 @ixek

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4 @ixek

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5 Top top view… @ixek Stable model/application ready to be productised R&D - develop, iterate fast, usually local or cloud Magic Is it live??

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6 How I would like everything to work…. @ixek It works…. now send it over to production R&D - develop, iterate fast, usually local or cloud Push code, tag, tag data* Worry free deployment! Wait and relax

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7 @ixek

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9 @ixek DevOps / DataOps / MLOps

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10 DevOps is the union of people, process, and products to enable continuous delivery of value into production What is DevOps anyway? @ixek

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11 Sort of DevOps applied to data-intensive applications. Requires close collaboration between engineers, data scientists, architects, data engineers and Ops. How does it fit for DS? @ixek

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12 @ixek Aims to reduce the end-to-end cycle time of data analytics/science from the origin of ideas to the creation of data artifacts.

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15 7 steps to DS

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16 Keep everything in source control - but allow for experimentation

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18 Standardize and define your environments in code (conda, pipfiles, Docker)

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19 Use canonical data sources - always know what data you are using (where it comes and goes)

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21 Automate wisely

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22 https://xkcd.com/1205/

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24 Use pipelines for repeatability and explainability

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25 Deploy portable models

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27 Test continuously and monitor production: shift left

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29 Thank you @ixek http://bit.ly/MancML-trallard