Taking ML to production - a journey

Go over some of the complexities of turning a machine learning solution to one used in production

Transcript

1. Taking ML to production - A journey Arnon Rotem-Gal-Oz
2. Mental model of the probelm Admission Intubation Alert >6 hours
3. Challlenge 1 defining the problem
4. A Perfect Representation of the Machine Learning Cycle from start to end | Image Source: MLOps (Published under Creative Commons Attribution 4.0 International Public License and used with attribution (“INNOQ”))
6. Challenge 2 – how we measure
7. Challenge 3 Data quality
9. Challenge 5 different types of data Model(s) text time series categorical
10. Challenge 6 labeling Admission Intubation
11. Challenge 7 dealing with imballance
12. Challenge 8 Model experimentation cycle
13. Modeling
14. Challenge 9 – Overfit
16. Moving to production…
17. Challenge 10 – model degredation in production Theory Reality
18. Challenge11 – Is it really generalized?
19. Challenge 12 model validation and verification
20. The road to production…