XGBoost is an open-source, scalable gradient-boosting library renowned for its speed, accuracy, and support for parallel, distributed and GPU-accelerated training.
LightGBM is an open-source gradient-boosting framework that delivers fast, memory-efficient tree-based learning for classification, regression and ranking tasks.
Open-source gradient-boosting library from Yandex that natively handles categorical features and offers fast CPU/GPU training.
A SaaS-first MLOps suite that tracks experiments, datasets and models while enabling collaborative LLM/GenAI application development.
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.
