>is "getting to prod" really the gatekeeper/bottleneck for most ML? The most common bottleneck is collecting the right data. It can take years, or even a task

>is "getting to prod" really the gatekeeper/bottleneck for most ML? The most common bottleneck is collecting the right data. It can take years, or even a task force just to get the right data before the data scientist can begin. >I would have thought "a model that works" is much harder It depends how experienced the data scientist is. Early on into a project a data scientist can do a feasibility assessment. They should identify what is possible, and how possible. Sometimes some data science projects are heavy on the research side where where it can take 2 weeks to 3 months to figure out if something is possible. Sometimes the feasibility assessment ends up being incorrect and a goal is shown to be impossible. Once research is done it usually takes 4 weeks to 6 months for a data scientist to build a model. The upper bound is rare and happens because of recursive refinement to increase accuracy, trying to get every last drop out of what is possible. In contrast it can take months to years for the company to begin to collect the right data for a data scientist to be able to begin to do what benefits the company. Sometimes crowd source projects need to be created just to collect the required data. It then takes an average of 3 to 6 months for productionization if there is clever feature engineering in the model. Note: When I say productionization, I mean all the way to the end customer, so setting up and maintaining pipelines, frontend devs updating websites to add the service, and whatever else is necessary. There is more work involved on the production side, but it can be split up to multiple engineers.