Evidently is an open-source ML observability platform that monitors data quality, data drift, model performance, and prediction quality in production. It generates interactive reports and dashboards, integrates with MLflow and Grafana, and supports real-time monitoring for LLMs and traditional ML models.
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Enterprise AutoML and AI lifecycle management
DataRobot automates the end-to-end machine learning lifecycle from data prep to model deployment to monitoring. Its AI Cloud platform supports all major ML frameworks and includes LLM ops for deploying and managing generative AI applications. Used by 40% of the Fortune 50.
Open-source AI and AutoML platform
H2O.ai provides open-source AutoML (H2O Driverless AI), LLM fine-tuning (H2O LLM Studio), and enterprise ML platforms. Driverless AI automatically engineers features, selects algorithms, and tunes hyperparameters to build models 40x faster than manual approaches.
MLOps platform for ML experiment tracking
Weights & Biases tracks ML experiments, visualizes training metrics, manages datasets, and profiles model performance in real-time. Teams at OpenAI, NVIDIA, and Toyota use it to collaborate on ML projects. Weave adds LLM evaluation, tracing, and monitoring to the platform.
The GitHub of machine learning models
Hugging Face hosts 500,000+ pretrained AI models, 150,000+ datasets, and 300,000+ demo apps — the central hub for the ML community. Inference Endpoints and AutoTrain enable anyone to fine-tune and deploy models without ML expertise. The most important infrastructure in open-source AI.