What I Do During A Data Science Project To Deliver Success
Eugene Yan
A data scientist describes practices for executing machine learning projects effectively, including conducting literature reviews before design, iterating quickly through experiments with Jupyter notebooks and MLflow, holding daily stand-ups and weekly end-of-day debriefs for team alignment, and conducting regular stakeholder check-ins with demos. The article emphasizes that a week of research into prior work is usually sufficient before starting an MVP, and recommends using simple tools like Flask or FastAPI for demos. The practices aim to prevent common pitfalls such as unnecessary reinvention, poor communication, and building features users don't want.
Why it matters
It's not enough to have a good strategy and plan. Execution is just as important.
