Notes on building machine learning systems

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

reference