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Demystifying Machine Learning in Production - Reasoning about a Large-Scale ML Platform
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- 1 Intro
- 2 4 things you can do for more reliable ML
- 3 ML on one machine
- 4 ML in production
- 5 What makes ML in prod interesting
- 6 What goes wrong?
- 7 4 things for more reliable ML
- 8 ML outages from the outside
- 9 Where changes happen: binaries
- 10 Where changes happen: configuration
- 11 Validating binary and config changes
- 12 Where changes happen: data
- 13 Validating data updates
- 14 Improving data integrity
- 15 Handling pipeline backlogs