A lot has already been said about the modern data stack (MDS) but the situation is significantly more scattered on the machine learning side of the fence: once data is properly transformed, how is it consumed downstream to produce business value? This post is intended for anybody wanting to bridge the gap between working with data and actually delivering business value using machine learning. The modern data stack (MDS) has been consolidated as a series of best practices around data collection, storage and transformation. A lot has been said already about the MDS as such, but the situation is more "scattered" on the other side of the fence: once data is properly transformed, how is that consumed downstream to produce business value? At the end of the day, ingesting and transforming data is not (for most companies) an end in itself: while tech giants figured out a while ago how to "get models in production", most companies still struggle to productionize a model in less than 3 months.