Ray is an open-source distributed compute engine that lets you scale Python and AI workloads—from data processing to model training and serving—without deep distributed-systems expertise.
CNCF-incubating model inference platform (formerly KFServing) that provides Kubernetes CRDs for scalable predictive and generative workloads.
Open-source, high-performance server for deploying and scaling AI/ML models on GPUs or CPUs, supporting multiple frameworks and cloud/edge targets.
An open-source platform from Databricks that manages the entire machine-learning lifecycle with experiment tracking, model packaging, registry and deployment.
OpenVINO is an open-source toolkit from Intel that streamlines the optimization and deployment of AI inference models across a wide range of Intel® hardware.
Microsoft’s high-performance, cross-platform inference engine for ONNX and GenAI models.
An Iguazio-backed open-source framework that orchestrates data/ML/LLM pipelines with serverless execution, tracking and monitoring.
Open-source, node-based workflow-automation platform for designing and running complex integrations and AI-powered flows.
NVIDIA’s model-parallel training library for GPT-like transformers at multi-billion-parameter scale.
Open-source framework for building, shipping and running containerized AI services with a single command.
Netflix’s human-centric framework for building and operating real-life data-science and ML workflows with idiomatic Python and production-grade scaling.
A Kubernetes-native workflow engine (originally at Lyft, now LF AI & Data) that provides strongly-typed, versioned data/ML pipelines at scale.
