Scientists in the field of machine learning (ML) – including deep learning (DL) -- aspire to build better models (usually judged by beating SOTA in well-defined tasks and datasets); successful applications of such models, on the other hand, are about product-market fit (PMF) in environments with ever-growing complexities. As many expect ML to play a bigger role in our society, ML scientists’ ability to influence this journey will depend on putting ML research in a PMF context and vice versa (i.e., optimising for market.fit(model.fit())+⍺*model.fit(market.fit()) instead of optimising for model.fit() alone). Therefore, in this tutorial we aim to cover the general principals of building AI products in the “real world”, covering topics such as product design/management, achieving product-market fit, and ML R&D in this context.


Schedule
All times are EST

+ Session 1 (11:00 a.m. - 11:15 a.m): Overview of tutorial and the core idea (R. Khorshidi)
+ Session 2 (11:15 a.m. - 11:45 a.m): Product Market Fit (R. Khorshidi)
- Break (11:45 a.m. - 12:00 p.m)
+ Session 3 (12:15 p.m. - 12:30 p.m): Build Measure Learn (R. Khorshidi)
+ Session 4 (12:30 p.m. - 1:00 p.m): Experiments and Metrics (R. Khorshidi)
- Break (1:00 p.m. - 1:15 p.m)
+ Session 5 (1:15 p.m. - 2:00 p.m): Examples (P. Faratin)
+ Q&A (2:00 p.m. - 2:15 p.m)