This applies also to ML - it applies to all tech projects, though yeah, it's harder in ML. But not figuring out the intermediate products is not an option though - your stuff will get killed prematurely if you don't.
The trick with ML is not to promise "98% precision and 92% recall by Q4", it's to figure out what kind of product is shippable with lower precision and recall. Or perhaps a stepping-stone model that allows some simpler use case, but gives you progress towards the greater goal.
It's always case-specific, but as a ML team you do need to figure out what your intermediate checkpoints are. You need to demonstrate not only progress, but that your progress is contributing to the company's goals.
Therefore, intermediate and measurable milestones are to derisk something. Even if you are doing a moonshot, there are still steps you can identify. The namesake, Apollo, didn’t try for the moon on the first launch!
- "You have already received benefit X for your investment in our team, and X has been well worth it. Continued investment in us will yield benefit Y."
- "Even if the project is terminated early, or later stages become blocked due to business or technical impracticalities, the company will have gained a tangible benefit already in the form of X. There is a payout, even if it is not the full thing we wanted."
- "The intermediate products validate the technology/product/business model, such that every incremental deliverable means an overall reduction in risk to the final goal."
That’s why my favorite interview question (I’ve done hundreds of interviews) is “tell me about a time you cut corners on a project”. My role as a data person is to provide enough signal as early as possible - not necessarily to give a precise answer to everything we want to know.
What you can do is estimate something like "we think we need $measurement_value on $metric by $date, we are at $current_value. If we're not at 75% of the gap in 50% of the project time (when we have low hanging fruit), we cancel."