When we reach for it
Any engagement training more than one model, which is any engagement training a model. Without it, “which version is live and how was it evaluated” becomes a question answered from memory, and the answer is often wrong.
MLflow
Experiment tracking and a model registry — the difference between a model you can reproduce and one you happen to have.
When we would argue against it
A single fine-tune that will never be repeated. Then a written record is enough, and we will not install a platform for it. Tracking experiments meticulously against a metric that does not reflect the business outcome is precise and useless, and no registry fixes that.
What it looks like in delivery
Every run logged with its data version, so the model in production can be traced to the code and data that produced it. Parameters and evaluation results recorded alongside it, so a regression can be traced to a specific change rather than argued about in a meeting.
Where this appears on the site
Nothing on this site names it yet
We work in MLflow, and no case study or service page currently published on this site prints it in its stack. Rather than describe an engagement you cannot check, this space stays empty until one does. Ask us and we will talk you through it directly.
Working in MLflow?
Tell us what it is running, what it costs you today, and what you need it to do next. A senior engineer will tell you what we would keep and what we would change.