Providers who deliver mlops & evaluation.

MLOps and evaluation is the class of systems that move a trained model from a notebook into a repeatable production surface and then keep watching it, data and feature pipelines, training and retraining orchestration, model registry and deployment, offline and online evaluation harnesses, and the observability layer that catches drift, degradation, and silent failure. It is infrastructure work; the model itself is usually the smallest part.

When evaluating MLOps providers, look past the demo dashboard. Ask how the evaluation harness is versioned alongside the model, a scorecard that changes without a git SHA is not an evaluation, it is a screenshot. Ask what "drift" specifically means in their model observability stack: input distribution drift, label drift, concept drift, and prediction drift are different problems and need different alerts. Ask how retraining is triggered and gated, and who signs off before a new model reaches live traffic.

The category is heavy on hype. A common anti-pattern is rebranding a generic logging or APM tool as ML observability without any label-aware metrics. Another is offering "automated evaluation" that only runs on a static holdout set and never touches production traffic. A third is treating ML production pipelines as one-way plumbing with no rollback path, a model gets deployed, and nobody can cleanly revert it when quality regresses.

Trustgent verifies providers in this category along the L0-L5 spectrum: L0-L1 is self-declared and public presence, L2 adds documented methodology, and L3+ requires third-party or evidence-backed attestation of the pipeline and evaluation practices themselves. Details at /how-we-verify.