MLflow.

Where MLflow fits on a long engagement.

Without tracking, a model in production is an artefact whose origin nobody can reconstruct. Which data, which parameters, which code — all of it lives in someone's memory until they leave. A registry turns that into a record, which matters most in exactly the regulated environments where it is most often missing.

The registry also makes promotion and rollback ordinary operations rather than events. Moving a model between stages and reverting when the new one underperforms should be as unremarkable as reverting a deployment.

What an assigned team does with MLflow.

Experiment tracking is adopted enthusiastically and abandoned quietly. It works while the person who set it up is still logging runs, and stops meaning anything within two quarters of them leaving, at which point the registry describes a past that no longer matches production.

Keeping it true is a habit rather than a tool, which is what continuous assignment actually buys. The wider platform work around it sits under DevOps as a service where the models run on shared infrastructure.

What we use MLflow for.

• Knowing what is actually deployed A record connecting the served model to the data, code and parameters that produced it.
• Comparing experiments honestly Runs tracked so a chosen model can be justified against the alternatives rather than asserted.
• Rolling back a model Reverting to a previous version as a routine action when a new one underperforms in production.

How MLflow capacity is assigned.

Tracking and registry work is assigned under MLOps engineering, inside your own infrastructure.