Neptune - mlops metadata store

Neptune is a metadata store for MLOps, providing experiment tracking, model registry, and monitoring capabilities for machine learning teams. Built for data scientists and ML engineers, Neptune helps track experiments, organize model development workflows, and monitor model performance in production. Key features include automatic experiment logging, collaborative workspaces, advanced filtering and search, visualization dashboards, and model versioning. Neptune integrates with 25+ popular ML frameworks including PyTorch, TensorFlow, XGBoost, scikit-learn, and Hugging Face. The platform offers features like experiment comparison, hyperparameter optimization tracking, artifact management, and custom dashboards. Neptune provides enterprise-grade security, compliance features, and scalable architecture that can handle thousands of experiments and team collaboration. With flexible deployment options including cloud and on-premise, Neptune helps organizations standardize their ML workflows, improve reproducibility, and accelerate model development cycles. Trusted by AI teams at startups and Fortune 500 companies for managing complex ML projects at scale.