TensorFlow specialists for your dedicated team.

Where TensorFlow fits in a long-term product team.

TensorFlow work is disproportionately maintenance of things that already run. A model trained four years ago, a serving setup left untouched since, and a version upgrade everyone has been deferring because the migration path is unclear. That is ordinary and it is where the value is.

Its deployment story remains a real strength. Serving at scale, running on mobile, and running in a browser are all better supported than in most alternatives, which is often why the estate is on TensorFlow and why moving off it is less attractive than it first appears.

What your dedicated team does with TensorFlow.

Maintaining models somebody else trained is the normal condition of an established ML estate, and it rewards familiarity more than brilliance. A version upgrade deferred for two years is a week of work for whoever knows the estate and a month for whoever does not.

When new feature work sits on top of the existing models, companies hire AI developers for it on the same agreement as the operational specialists.

Common TensorFlow use cases.

• Upgrading a pinned version Moving off an old release with a tested path.
• Running models on device Deployment to mobile or edge where latency or privacy rules out a round trip.
• Retraining an ageing model Refreshing a model whose accuracy has drifted, with evaluation that proves the new one is better.

Adding TensorFlow skills to your team.

Production model work sits with a dedicated MLOps team, because the challenge is usually operational.

TensorFlow 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.