digging for requirements
- what exactly is the business objective?
- how does the company expect to use and benefit from this model?
workflow to Approach a ML problem -> Prototype
-
what kind of question or goal we wanna answer
-
how to define and measure success -> like using a business metric like increased profit or decreased losses
-
acquire the data and build a working prototype - a loop [TODO]
- analyze the mistakes
- collect more or diff data
- change the task formulation slightly
-
humans in the loop
- algotithms might increase response time or reduce cost
- TODO
From Prototype to Production
- data analytics teams
- production teams -> reimplement the solution for robust, scalable system
- offline evaluation
- online testing using A/B testing