SLIDE 1 1
Instrumentation, Observability, and Monitoring of Machine Learning Models
SLIDE 2 About Me
(2007-11)
Data Science (2011-15)
Engineering (2015-2017)
SLIDE 3
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SLIDE 5
The Genesis of This Talk
SLIDE 6
Machine Learning In the Wild
SLIDE 7
Data Science Meets DevOps
SLIDE 8
Some History
SLIDE 9
Logs via the ELK Stack
SLIDE 10
Metrics with Prometheus
SLIDE 11
Prometheus Architecture
SLIDE 12
Traces
SLIDE 13
A Word About Cardinality
SLIDE 14
Make Good Decisions By Avoiding Bad Decisions
SLIDE 15
The ML Test Score
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The Map Is Not The Territory
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Monitor Model Decay
SLIDE 18
Build Lots of Models
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Deploy Your Models Like They Are Code*
SLIDE 20 Stand On The Shoulders of Giants
- Ensembles
- Experiments
- Dark Tests
- Canary
- Sanity Checks
SLIDE 21
Tag All The Things
SLIDE 22
Circle of Competence
SLIDE 23
Garbage In...
SLIDE 24
Linking Online and Offline Metrics
SLIDE 25
Handling Cross-Language Feature Engineering
SLIDE 26
Know Your Dependencies
SLIDE 27
Monitoring For Critical Slices
SLIDE 28
Second-Order Thinking
SLIDE 29
On Razors
SLIDE 30 http://slack.com/careers
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