I work for a large company, with a centralized data science org, as well as several teams which are using or plan to use some machine learning or statistical modeling. The tools right now are disparate, but very little of it is in production, so it doesn’t matter that much.

(Some) Leaders are pushing for a unified ML platform to be used by all teams, and it will either be something built in house on top of AWS, or some sort of off the shelf tool like Databricks, or H2O, or what not. Based on the current level of discussion, the organization is at least a year out from now. We can wait for it, but we will essentially twiddle our fingers for a good part of our projects while waiting.

We have a project which is moving forward pretty fast, and we could just go ahead and build it using AzureML studio, with is what my team’s engineers are the most familiar with. But if the rest of the company gets their act together and eventually comes up with a unified ML platform, we will be completely out of synch with them, and we end up with a ton of technical debt.

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Does anybody have experience with this dilemma? How do you keep your ability to move quickly with your own project, while still conforming to the company’s overall unified ML platform?