TensorFlow Serving is a high-performance serving system for machine learning models designed for production environments.
The tool addresses the inference phase of machine learning workflows, taking trained models and managing their deployment lifecycle. It provides versioned model access through a reference-counted lookup table, exposing both gRPC and HTTP endpoints for client inference requests. While built with native TensorFlow model support, the system can be extended to serve other model types and data formats. A key performance feature is its request batching scheduler, which groups individual inference requests for joint GPU execution with configurable latency controls, minimizing overhead during inference.
Adoption suits teams deploying models to production who need multi-version model serving, canary deployments, and A/B testing capabilities. The tool supports serving multiple models or model versions simultaneously without requiring client code changes when new versions are deployed. Docker-based deployment is the recommended path for most users, though source builds and Kubernetes deployments are documented alternatives. The system can serve TensorFlow models, embeddings, vocabularies, feature transformations, and non-TensorFlow machine learning models as servables.
Maintainers respond to new issues and pull requests within a few days. The issue tracker shows active engagement with feature requests and community contributions, with work dominated by feature development and requests for community contributions.
