An open-source, production-ready system for serving machine-learning models at scale.
A lightweight open-source platform for running, managing, and integrating large language models locally via a simple CLI and REST API.
TensorFlow Serving is Google’s high-performance inference engine designed to move trained ML/DL models from the lab to production with minimal friction. It provides a ModelServer binary (and pre-built Docker images) that exposes both gRPC and REST endpoints, supports transparent model versioning and hot-swapping, and delivers optimized CPU/GPU execution paths. While it offers first-class, out-of-the-box support for TensorFlow’s SavedModel format, its plugin architecture lets teams extend Serving to other frameworks or custom data sources. Tight integration with the wider TFX ecosystem, Kubernetes deployment guides, and a battle-tested track record inside Google (hundreds of internal workloads) make it a trusted choice for real-time and batch inference in cloud and on-prem environments.
Typical use-cases include A/B testing new model versions, rolling upgrades without downtime, and serving ensembles of models behind a single endpoint—all while preserving low-latency, high-throughput guarantees. Comprehensive tutorials, reference configurations, and community-maintained extensions further lower the operational barrier for data-science and MLOps teams.
