Comet.
Comet is an experiment management platform covering tracking, model production monitoring, dataset versioning and artefact management, with both cloud and self-hosted deployment.
Where Comet fits on a long engagement.
Comet's distinguishing feature is spanning the whole lifecycle rather than stopping at training. Tracking experiments and monitoring production model behaviour in one platform means the comparison between how a model scored and how it performs is available rather than assembled.
That continuity matters because the two are so often disconnected. A model with excellent offline metrics performing poorly in production is the standard machine learning disappointment, and diagnosing it needs both sets of numbers in the same place.
What an assigned team does with Comet.
Self-hosting is available, which decides the choice where training data or production inputs cannot go to a third-party service.
Making that decision on data-protection grounds rather than convenience is part of responsible scoping, and what it covers is set out in how the monthly fee is built.
What we use Comet for.
- Offline scores compared to production behaviour Both in one place, which is what makes the usual disappointment diagnosable.
- Datasets versioned alongside runs A complete record of what each model was trained on.
- Self-hosted where data cannot leave Deployment inside the estate when inputs are sensitive.
How Comet capacity is assigned.
Experiment platform selection is assigned inside AI capacity, with hosting decided on data-protection grounds.
Tell us what your roadmap needs Comet for.
A service delivery manager replies with the disciplines we would assign, the monthly capacity and what the first month looks like.
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