TensorFlow Serving

A flexible, high-performance serving system designed specifically for machine learning models in production environments. Provides efficient model version management, RESTful and gRPC APIs, and seamless integration with TensorFlow ecosystem for scalable ML inference.

Use it when

• •Production deployment of TensorFlow models requiring high-performance inference
• •Model version management with automated rollout and rollback capabilities
• •Serving multiple model versions simultaneously for A/B testing and gradual deployment
• •High-throughput inference workloads requiring optimized batch processing
• •Microservices architecture requiring containerized ML model serving
• •Real-time inference applications needing low-latency gRPC endpoints
• •MLOps pipelines requiring separation between model training and serving code
• •Kubernetes-based deployments requiring scalable, cloud-native model serving

Watch out

• ⚠Limited to TensorFlow models - not suitable for PyTorch, scikit-learn, or other frameworks
• ⚠Complex configuration for advanced use cases like custom preprocessing
• ⚠Learning curve for gRPC API compared to simple REST endpoints
• ⚠Resource overhead may be excessive for simple models or low-traffic scenarios
• ⚠Monitoring and observability require additional tooling and configuration
• ⚠Version management complexity increases with multiple models and environments
• ⚠Custom business logic requires writing custom ops or external preprocessing
• ⚠Debugging inference issues can be challenging in distributed deployments

Available in stages

Installation

Example stacks