MLOps agencies: hire for production ML infrastructure

DevOps ships application releases; MLOps adds the model lifecycle — data and feature versioning, experiment tracking, model registries, eval gates, training/inference pipelines, drift and quality monitoring, and safe rollback when predictions degrade. Overlap with infra and CI is real, but a strong MLOps partner obsesses over model promotion criteria and post-deploy quality, not only container deploys. If a shop only talks Kubernetes and never mentions evals, drift, or registry ownership, they are selling DevOps with GPUs attached.

Hire MLOps when you already know the model (or a close baseline) and the pain is reproducibility, deployment, monitoring, or multi-model ops. Hire modeling / data science when you still need feature design, algorithm selection, and proof the model beats your current process. Hybrid is common: modelers define the target metric and features; MLOps industrializes the path. Write the split into the first SOW so you do not pay for a “platform” while the model itself is still unproven.

Prefer one critical path: reproducible train → evaluate → register → deploy → monitor → rollback for a named model, in accounts you control, with a short runbook and promotion criteria. Platform roadmaps (feature stores, multi-tenant serving, full governance) belong in a later phase after that path works. Distrust fixed bids for “enterprise MLOps maturity” with no named workflow; insist on environments, credential ownership, and who on-calls when quality drifts.

Pick operating metrics tied to risk: deploy lead time, failed release rate, time-to-rollback, eval pass rate before promotion, drift or quality alerts acknowledged within an agreed window, and unit cost per prediction or training run. Secondary metrics (tool adoption, dashboard count) are vanity without safer, faster model changes. Require a baseline and a re-measure plan in the SOW; if the agency cannot define “good” without buzzwords, keep shopping.

You keep cloud accounts, Git org, model registry, secrets, monitoring workspaces, and production deploy rights. The agency owns implementation against the SOW, pipeline quality, and documented handoff — including how to promote and roll back without them. Never leave training data access or API keys solely under the vendor’s org; require exit terms with exports and credential rotation.