tensorflow serving
FIPS 140-3Machine learning & AIA flexible, high-performance serving system for machine learning models designed for production environments
A flexible, high-performance serving system for machine learning models designed for production environments
TensorFlow Serving is a flexible, high-performance serving system for machine learning models designed for production environments. It is part of the TensorFlow Extended (TFX) platform and provides a robust infrastructure for deploying machine learning models at scale.
This Ghost hardened image provides the CPU variant of TensorFlow Serving, optimized for inference workloads on CPU-based infrastructure.
TensorFlow Serving supports model versioning, allowing you to deploy new model versions without taking down the service. It automatically manages model lifecycle, loading new versions and unloading old ones based on configurable policies. The system is optimized for low latency and high throughput, making it suitable for production workloads.
TensorFlow Serving provides both REST and gRPC APIs for making predictions. The REST API is convenient for development and testing, while the gRPC API offers better performance for production deployments. The system supports batching multiple requests together to improve CPU utilization and overall throughput.
TensorFlow Serving is widely used in production systems for serving recommendations, image classification, natural language processing, and other machine learning applications at scale.
For more details, visit https://www.tensorflow.org/tfx/guide/serving.