Comet specialists for your dedicated team.

Where Comet fits in a long-term product team.

Comet's distinguishing feature is covering the whole lifecycle, from training into production. Tracking experiments and monitoring production behaviour in one platform puts how a model scored and how it performs side by side.

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 your dedicated 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 is part of responsible scoping, as set out in how the monthly fee is built.

Common Comet use cases.

• 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.

Adding Comet skills to your team.

Experiment platform selection is assigned inside AI capacity, with hosting decided on data-protection grounds.

At Azendo, Comet work sits with a dedicated MLOps team. Comet specialists join your team remotely as dedicated, full-time team members, selected, employed and managed by us in Chiang Mai and Bangkok. Companies that hire offshore developers through Azendo add the skill at the next monthly cycle.