MLOps Platform Build Playbook

MLOps Platform Build Playbook

This playbook describes how to stand up a production-grade MLOps platform. MLOps (machine learning operations) applies DevOps discipline to the ML lifecycle: data preparation, training, evaluation, deployment, and monitoring. The goal is repeatable, governed, and fast delivery of models to production. This playbook suits organizations moving from notebook-driven experiments to a managed platform serving multiple teams.

Phase-by-Phase

Discovery and Readiness. Assess current ML maturity. Inventory existing model workloads, their data sources, and how they reach production today. Define the platform scope: which capabilities are in-house versus managed services. Produce a maturity assessment and a scope document that frames the build.

Foundation and Data Layer. Build the feature store so teams share consistent features across training and serving. Establish data contracts between producers and consumers to prevent silent schema breaks. Adopt data version control (DVC) so datasets are reproducible alongside code.

Training and Registry. Automate training pipelines as code so runs are reproducible and auditable. Stand up a model registry to track versions, lineage, and approval state. Enforce reproducible builds so any model can be rebuilt from a commit.

Serving and Monitoring. Deploy model serving behind versioned endpoints. Instrument drift detection to catch data and concept drift early. Define service level objectives (SLOs) for latency and availability, and roll out new models with canary releases.

Operationalize and Scale. Enable self-service so teams onboard without platform-team bottlenecks. Establish governance aligned to ISO/IEC 42001. Optimize cost with FinOps practices, since GPU spend grows quickly.

Team and Roles

An architect owns the platform design. Data engineers build the feature and pipeline layers. DevOps engineers automate CI/CD and infrastructure. SREs own reliability, SLOs, and on-call. A product owner prioritizes platform capabilities against team demand.

Risks and Mitigations

• Data consistency between training and serving causes silent accuracy loss; mitigate with a shared feature store and data contracts.
• Skills gap slows adoption; pair platform engineers with ML practitioners and document golden paths.
• Team coordination across data, ML, and ops can stall delivery; use clear ownership and a thin platform team with self-service.

Success Criteria

Track deployment frequency for models, model time to production, and platform adoption across teams. A healthy platform reduces lead time from weeks to days while increasing audit coverage.

Tooling

Kubernetes orchestrates training and serving. Python is the primary ML language. ArgoCD delivers via GitOps. Prometheus and Grafana provide metrics and dashboards for both infrastructure and model health.