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Instrumentation, Observability, and Monitoring of Machine Learning Models 1 About Me Google Engineer (2007-11) Clouderas Director of Data Science (2011-15) Slacks Director of Data Engineering (2015-2017) Slack Engineer


  1. Instrumentation, Observability, and Monitoring of Machine Learning Models 1

  2. About Me ● Google Engineer (2007-11) ● Cloudera’s Director of Data Science (2011-15) ● Slack’s Director of Data Engineering (2015-2017) ● Slack Engineer (now)

  3. “”

  4. “”

  5. The Genesis of This Talk

  6. Machine Learning In the Wild

  7. Data Science Meets DevOps

  8. Some History

  9. Logs via the ELK Stack

  10. Metrics with Prometheus

  11. Prometheus Architecture

  12. Traces

  13. A Word About Cardinality

  14. Make Good Decisions By Avoiding Bad Decisions

  15. The ML Test Score

  16. The Map Is Not The Territory

  17. Monitor Model Decay

  18. Build Lots of Models

  19. Deploy Your Models Like They Are Code*

  20. Stand On The Shoulders of Giants ● Ensembles ● Experiments ● Dark Tests ● Canary ● Sanity Checks

  21. Tag All The Things

  22. Circle of Competence

  23. Garbage In...

  24. Linking Online and Offline Metrics

  25. Handling Cross-Language Feature Engineering

  26. Know Your Dependencies

  27. Monitoring For Critical Slices

  28. Second-Order Thinking

  29. On Razors

  30. http://slack.com/careers 30

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