Choosing the right abstraction for your problem - it is a step crucial to the success of every ML project, yet one that is often overlooked. We could spend hours debating whether to use XGBoost or CATboost, yet neglect giving our conscious attention to a more elementary decision: how to model our problem in the first place.
The exact same business case could be modeled, for example, as a classification problem, a clustering one, or even a graph link-predictions task. And as the options vary, so do the considerations for choosing among them, including not only machine learning theory, but also the needs of your business, organizational constraints, and many more.
Join me to discuss the principles of choosing the best ML abstraction for your needs, things to consider and pitfalls to avoid, all through the lens of a real-life case study from my consultancy work.

Outline:
• Self intro (3 minutes)
• What do I talk about when I talk about “choosing the right ML abstraction” (5 minutes)
◦ (Explain concept and how it relates to business strategy)
• Go through a use case (15 minutes total)
• Overview of the business case (5 minutes)
• Modeling alternatives: (7 minutes)
◦ First iteration: go through just ML pros and cons
◦ Then, another iteration – adding business needs pros and cons
• Present EL’s final decision & explain reasoning - 3 min
• Summary & take-home message (2 minutes)
• Q&A (5 minutes)