DVC specialists for your dedicated team.

Where DVC fits in a long-term product team.

Reproducibility in machine learning requires versioning data as well as code, and Git alone cannot hold multi-gigabyte files. DVC resolves that by committing content-addressed pointers and keeping the data in object storage, so checking out an old commit retrieves the data that commit used.

Its pipeline definitions add dependency tracking, so a stage reruns only when its inputs change. On expensive training pipelines that avoids recomputing stages whose inputs are identical, which is a substantial saving in both time and compute.

What your dedicated team does with DVC.

Storage discipline is required. Every version of every dataset builds up in remote storage, and without a retention policy the cost grows steadily for data that will never be checked out again.

Deciding what to keep and for how long is ongoing housekeeping, held within an agreed committed monthly capacity.

Common DVC use cases.

• Data retrievable per commit Checking out an old commit and getting the data it was built against.
• Stages rerun only when inputs change Dependency tracking that avoids recomputing expensive steps unnecessarily.
• Storage retention managed A policy for old versions, because they accumulate indefinitely otherwise.

Adding DVC skills to your team.

Data versioning sits with the ML specialists we assign to you, with a storage retention policy agreed at setup.

At Azendo, DVC work sits with a dedicated MLOps team. DVC 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.