Basics of Data Science Product Management: The ML Workflow

I've spent the last few years applying data science in different aspects of business. Some use cases are internal machine learning (ML) tools, analytics reports, data pipelines, prediction APIs, and more recently, end-to-end ML products. I've had my fair share of successful and unsuccessful ML products. There are even reports of ML product horror stories where the developed solutions ended up failing to address the problems they were supposed to solve. To a large extent, the gap can be filled by properly managing ML products to ensure that it ends up being actually useful to users. Given the difficulties in the ML workflow and our resource constraints (e.g. In this blog post I aim to give an overview of each of these steps, while illustrating some of the foreseeable challenges and the frameworks that I've found to be useful in optimizing the ML workflow.