Neptune.ai specialists for your dedicated team.
Neptune.ai is a metadata store for machine learning, logging experiments, model versions, datasets and artefacts with a flexible structure and a focus on handling very large numbers of runs.
Where Neptune.ai fits in a long-term product team.
Neptune is built for volume. Teams running thousands of experiments, with extensive hyperparameter search, many model variants and continuous retraining, find that platforms built for hundreds become slow to query, just when the metadata store matters most.
Its structure is deliberately flexible, which suits teams with unusual requirements and means the organising conventions have to come from the team.
What your dedicated team does with Neptune.ai.
Metadata is most valuable at scale and most neglected there. A team running thousands of experiments without consistent naming and tagging ends up with a large store of runs that are hard to find.
Agreeing those conventions early and keeping to them is a discipline our specialists hold as part of a dedicated MLOps team.
Common Neptune.ai use cases.
- Thousands of runs still queryable A store that stays fast at the scale where metadata matters most.
- Structure suited to unusual workflows Flexible organisation where a prescriptive tool would not fit.
- Conventions agreed up front Naming and tagging held consistently, or scale makes the store unsearchable.
Adding Neptune.ai skills to your team.
Metadata management is assigned inside AI capacity, with naming conventions agreed before volume makes them unenforceable.
You can add Neptune.ai capacity as one dedicated specialist or as part of a wider remote team, managed by a service delivery manager from our offices in Thailand. Companies hire dedicated developers this way under one agreement and one monthly fee.
Tell us what your roadmap needs Neptune.ai for.
A service delivery manager will reply with the specialists we'd suggest, the monthly capacity and what the first month looks like.
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We reply within one business day.
