The Post-Modern Stack

If you followed us closely, Episode 4 brought us to the limit of Data Land and at the start of ML Land: it is now time to close the circle, and take those nicely transformed data rows into a machine learning model serving predictions to users. Clone the repo, check the video, buckle-up, and join us for one last trip together. The modern data stack (MDS) has been consolidating a number of best practices around data collection, storage and transformation. The web is full of examples (including our own!) of how to set up the MDS. However, they may leave you wondering what happens "on the ML side": once data is pre-aggregated and features pre-computed, how is that consumed downstream to produce business value? This post sets out to answer this question, by proposing a lightweight toolchain that leverages Metaflow as the backbone for ML operations: the community reaction to the "Bigger boat" repo has been overwhelmingly positive, but we thought we should also put forward a low-touch alternative for teams that want quicker start.