CI/CD for models specialists for your dedicated team.

Where CI/CD for models fits in a long-term product team.

Model deployment differs from application deployment in one decisive way: correctness is statistical. Tests do not pass or fail; a model scores, and the pipeline needs a threshold policy deciding whether that score is good enough to promote.

Comparison against the current production model is the gate that matters. A new model must beat what is already deployed on the metrics that count, evaluated on the same held-out data, or promoting it is a change with no evidence behind it.

What your dedicated team does with CI/CD for models.

Rollback needs to be as fast for models as for code. When a deployed model starts behaving badly, reverting to the previous version should be a single operation.

That means retaining previous model artefacts and keeping the deployment path reversible, which is part of the delivery standard agreed as a committed monthly capacity.

Common CI/CD for models use cases.

• Promotion gated on evaluation A threshold policy, so a model reaches production on evidence.
• Compared against what is deployed The incumbent as the baseline, evaluated on the same held-out data.
• Rollback as one operation Previous artefacts retained, so reverting does not mean retraining.

Adding CI/CD for models skills to your team.

Model delivery pipelines span ML and platform capacity, with rollback as a required capability.

At Azendo, CI/CD for models work sits with a dedicated MLOps team. You can add CI/CD for models capacity as one dedicated specialist or as part of a wider remote team, with the people managed by us from our offices in Thailand. Companies hire dedicated developers this way under one agreement and one monthly fee.