Feature stores and platforms — the DoorDash arc, Airbnb's Chronon, Uber's Michelangelo — plus training-data pipelines and the AI-era stack.
MLOps is mostly data engineering. The proof is the feature-platform canon: DoorDash's Riviera→Fabricator→Workbench arc, Airbnb's Chronon (run at Stripe too), LinkedIn's Feathr, Uber's three Michelangelo generations, and the embedding-era stack — where interviews are heading as DE and ML infra converge.
The generations law (rewrite on workload-class change, extend when API-shaped), adoption as an org variable, the build/buy boundary migrating toward abstractions, and UX as the last bottleneck.
The ML platform team presents its third roadmap in four years. The first platform standardized everyone onto shared pipelines — then deep learning arrived and none of it fit. The second rebuilt for GPUs — adoption stalled at a handful of teams for a year, then mysteriously hit 95% the next. Now generative AI is here, product teams are calling external APIs from laptops, and the platform lead proposes rewrite number three. The CTO asks the only question that matters: *why will this one be different?* The honest answer requires knowing why the last two went the way they did. Do you?
The full week 3 brief is part of LeetData Pro.
