TensorFlow Serving specialists for your dedicated team.

Where TensorFlow Serving fits in a long-term product team.

It is mature and efficient at what it does. Automatic version discovery, where a new model directory is loaded and traffic moves over without a restart, makes model updates routine.

The SavedModel format bundles the graph and weights together, which removes a class of mismatch between code and artefact. The signature definition also makes the serving interface explicit.

What your dedicated team does with TensorFlow Serving.

Being framework-specific is the trade. It serves TensorFlow well and nothing else, so an organisation with mixed frameworks operates more than one serving stack.

Whether that specialisation justifies the extra surface is an architectural judgement made at scoping, as described in how it works.

Common TensorFlow Serving use cases.

• Model updates without a restart New versions discovered and loaded automatically.
• An explicit serving interface Signature definitions that state the contract.
• Efficient batching built in Throughput handled by the server itself.

Adding TensorFlow Serving skills to your team.

Serving infrastructure is assigned inside AI capacity, with framework-specific and general servers weighed against total operational surface.

At Azendo, TensorFlow Serving work sits with a dedicated MLOps team. TensorFlow Serving 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.