ML Infrastructure Roadmap

• Sequence a from-zero-to-platform migration
• Size each step so it pays back before the next begins
• Avoid the rewrite trap while building a real platform

• Big-bang platform rewrites fail because teams stop trusting the platform they didn't help build.
• Incremental steps de-risk: each migration has a smaller blast radius and a measurable win.
• The data proves where the money should go — incidents and toil are the roadmap's signal.
• A presentable sequence keeps stakeholders aligned on why 'next' is the right next.

You don't knock the house down and rebuild — you fix the roof, then the plumbing, one livable room at a time. ML platform migration is renovation in production.

• Step 1, stabilize: introduce tracking and versioning where teams already work; no tool diktat, just record-keeping that pays off.
• Step 2, centralize: stand up the model registry and make it the deploy source, closing side-door deployments.
• Step 3, standardize serving: one serving runtime and API contract so every model gets the same latency/health behavior.
• Step 4, automate: CI/CD for models with tests and gates; training runs become pipeline steps, not notebooks.
• Step 5, operate: live monitoring, promotion alerts and an incident workflow; then continuous training is finally safe to add.

• A startup's first platform step was a shared tracking server — data scientists adopted it because they could finally compare pressure-tested runs, and the platform team got the lineage it needed for CI/CD later.
• A mid-size firm centralized the registry before standardizing serving, so each new model deployment got automatic stage tracking and audit history without a manual wiki.
• A large org phased rollout by team cohort: three pilot teams ran the platform for a quarter, published perf numbers, and the rest of the org adopted with evidence.

• Start with toil: pick the step that removes the most manual work today, not the most fashionable tool.
• Make each step reversible — keep a documented fallback path so teams don't fear committing.
• Publish before/after numbers per step; adoption follows evidence.
• Timebox each step; platform work without a deadline becomes a permanent side project.

• Refactoring everything into new abstractions before any team adopted the platform — a rewrite, not a migration.
• Mandating change without a harness: teams that can't see their own win quietly never migrate.
• Skipping the serving standardization, then discovering every model is its own API shape again.

• If adoption stalls, look at the harness — a model team that can't onboard in half a day won't.
• If a step produced no incident reduction, re-measure the toil it claimed to remove.
• Watch for shadow platforms: teams building their own registry/serving because the official one doesn't meet them.