#Model Inference

BentoML is an open-source Python framework crafted for creating and deploying AI model serving systems. It enables seamless conversion of model scripts into REST API servers, supporting diverse ML frameworks and runtimes, while simplifying dependency management through straightforward configuration files. Optimized for high-performance, BentoML enhances resource usage with features like dynamic batching and model parallelism. Deploy models effortlessly using Docker containers or integrate smoothly with BentoCloud, offering a versatile solution for both local and production settings.

Multi Model Server is a versatile platform for serving deep learning models across different ML/DL frameworks. It includes server CLI, Docker images, and HTTP endpoints for inference in CPU and GPU setups. Features include rapid model deployment, API-based management, and streamlined model archiving. Enhanced security and deployment options are provided through Docker applications. Comprehensive documentation and community support are available.

KServe provides an efficient solution for deploying scalable machine learning models on Kubernetes. It integrates seamlessly with popular ML frameworks like Tensorflow, PyTorch, and others, offering features such as GPU autoscaling and Canary Rollouts for enhanced model management. Designed for modern applications, it supports cloud-agnostic and serverless deployments with ease of integration and deployment as key benefits.