Building an Internal ML Platform

By this point the course has covered the individual capabilities of MLOps, tracking, versioning, training, serving, monitoring, CI/CD, governance, but in a real organisation a dozen teams each assembling these from scratch produces a dozen incompatible, half-built stacks, duplicated effort, and inconsistent practices. An internal ML platform is the answer: a shared, paved-road set of services and tooling that lets data scientists go from idea to production model without reinventing the infrastructure each time. The problem it solves is the enormous duplication and fragmentation of ad-hoc MLOps: without a platform, every team rebuilds pipelines, picks different tools, and re-solves the same serving and monitoring problems, while the organisation has no consistent governance or visibility. A platform consolidates this into self-service infrastructure with sensible defaults, so practitioners focus on models, not plumbing. Understanding ML platforms matters because it is the capstone perspective of MLOps: the discipline is not just a toolbox but an integrated capability, and at scale the question shifts from how to do each task to how to provide all of them as a coherent, governed, self-service product that multiplies the productivity of every team building on it.