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Practical DevOps for the busy data scientist
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Tania Allard
June 27, 2019
Technology
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Practical DevOps for the busy data scientist
Tania Allard
June 27, 2019
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Transcript
Tania Allard, PhD @ixek Developer Advocate @Microsoft Practical DevOps for
the busy Data Scientist http://bit.ly/MancML-trallard
2 A bit of background never hurt anyone About us
3 @ixek
4 @ixek
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??
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
7 @ixek
8
9 @ixek DevOps / DataOps / MLOps
10 DevOps is the union of people, process, and products
to enable continuous delivery of value into production What is DevOps anyway? @ixek
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
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.
13
14
15 7 steps to DS
16 Keep everything in source control - but allow for
experimentation
17
18 Standardize and define your environments in code (conda, pipfiles,
Docker)
19 Use canonical data sources - always know what data
you are using (where it comes and goes)
20
21 Automate wisely
22 https://xkcd.com/1205/
23
24 Use pipelines for repeatability and explainability
25 Deploy portable models
26
27 Test continuously and monitor production: shift left
28
29 Thank you @ixek http://bit.ly/MancML-trallard