[hectormalot]

Agree. We do something similar:

* 1 month (or less) to prove that it makes sense to continue. We do start out with a pretty well defined problem definition though. Outcome is typically a bunch of jupyter notebooks proving that we get at least some predictive value that helps solve the problem. If no proof: no-go

* 2 months to upgrade it to something that works well enough to run a real pilot (e.g. shadow run, run with a few agents, in an A/B test, etc.). If no success: no-go

* Then hopefully in production within another 3 months. We're in a pretty regulated industry, most of this time is _not_ engineering.

The phases help set priorities as well. Doesn't always make sense to spend (much) more time on scalability if we don't know it works yet.

[apohn]

>We do start out with a pretty well defined problem definition though.

One of the things that really helps this happen is when the management structure (on both sides) understands a defined problem statement that both sides agree to is a requirement for a project to have any hope of success.

The above isn't specific to Data Science projects. It's just having a management structure who knows the right time to support things being pushed back.