MLOps & Platforms

The practices that make ML reproducible, testable, deployable and observable — CI, CT and CD distinguished, platform capabilities, cloud primitives before vendors, cost.

What MLOps Is

Engineering practices and platform capabilities that make ML systems reproducible, testable, deployable, observable and maintainable. Not a product, and not a cluster.

Q · A team has three models in production that nobody can retrain without the original author. What is actually missing — and why is the answer not "adopt a platform"?
The MLOps Pipeline
▶ lab

Data → Validation → Training → Evaluation → Artifact → Registry → Deployment → Monitoring. Each stage has a way of failing that the next stage cannot see.

Q · A model went from raw data to production through eight stages. Which stage let the bad model through, and what would have stopped it there?
CI, CT and CD for ML
▶ lab

Three different loops with three different triggers. CI proves the code and data are sound; CT proves a new model can be trained; CD proves it is safe to serve. A green one proves only its own claim.

Q · The CI pipeline is green, the model retrained overnight, and the deployment succeeded. Which of those three facts says the model in production is good — and which questions does each one actually answer?
ML Platform Engineering

Shared capabilities — dataset access, feature pipelines, training jobs, tracking, registry, serving, monitoring, GPU scheduling — built once for many model teams. Premature before the third model.

Q · Four teams each built their own training and serving path. When does a shared ML platform pay for itself, what does it own, and where does its responsibility stop and the model team's begin?
Cloud ML Services

Every managed ML service is an implementation of a primitive you should already be able to name — a training job, a GPU, a registry, a hosted endpoint. Learn the primitive, then map the vendor.

Q · A cloud provider offers a managed training service, a model hosting service and a feature store. What is each one actually doing underneath, what does it hide, and what do you need to check before trusting it?
ML Orchestration

A DAG of data → features → train → evaluate → register → deploy, with scheduling, retries and backfills. The ML-specific hazards: a training step that is not idempotent, an evaluation gate, and artifact promotion as a step.

Q · The orchestrator retried a failed training step and the registry now holds two candidates from the same run. What about ML steps makes the standard orchestration assumptions wrong?
ML Cost Optimisation
▶ lab

Before buying cheaper GPUs, ask whether a simpler model works, whether training can happen less often, whether inference can be batched, quantized, cached or precomputed. Utilisation is the lever.

Q · The ML bill tripled and the first proposal is a cheaper GPU tier. Which questions come before that, and which number actually decides the bill?