Explain ML Project Milestones, Scope, and Conflict

Describe an ML project you worked on, focusing on how you defined its scope, chose milestones, and handled a disagreement that affected delivery. Explain your contribution and how the project plan changed as evidence arrived. ### Constraints & Assumptions - Use your own project and distinguish your decisions from team decisions. - Explain the ML outcome and the product or operational outcome separately. - Describe milestones with observable acceptance criteria rather than only dates or broad labels. - Discuss one actual disagreement over scope, priorities, technical direction, or evaluation. Do not invent conflict or quantitative results to make the story more dramatic. ### Clarifying Questions to Ask - Should the deep dive emphasize project planning, technical evaluation, or the disagreement and its resolution? - Would a brief overview of the data, model, and production path help establish context? - Which milestone or scope change would the interviewer like to examine in detail? ```hint Connect the plan to a decision For each milestone, identify what its result allowed the team to decide next, including whether to stop or reduce scope. ``` ### What a Strong Answer Covers - The initial goal, project boundaries, dependencies, and personal ownership. - Milestones tied to data feasibility, model quality, and operational readiness. - Competing positions in a disagreement, including the legitimate concern behind each. - Evidence used to choose an action, the resulting scope decision, and communication to affected people. - An honest outcome and a lesson grounded in that sequence of decisions. ### Follow-up Questions - Which milestone would you change if the same project started again? - What would you do if the model met an offline target but failed the product or operational target?