Experiment Tracking
Showing 12 of 12 projects
An open-source AI engineering platform for debugging, evaluating, monitoring, and optimizing production LLM applications and machine learning models.
A Python framework for creating reproducible, maintainable, and modular data engineering and data science pipelines.
A framework for elegantly configuring complex applications, particularly in machine learning and research.
A library that enables PyTorch, Chainer, MXNet, and NumPy users to write TensorBoard events with simple function calls.
A suite of web applications for inspecting and understanding TensorFlow runs and graphs.
An open-source MLOps/LLMOps suite for experiment management, data management, pipelines, orchestration, scheduling, and model serving.
An open-source, self-hosted ML experiment tracker with a performant UI and SDK for comparing and querying training runs.
An open-source MLOps platform for building, orchestrating, and deploying production AI pipelines and agents.
An open-source platform for building, training, and monitoring large-scale deep learning applications with full lifecycle MLOps.
A TensorFlow project template with a well-designed folder structure and OOP design to accelerate deep learning development.
An open-source machine learning platform for distributed training, hyperparameter tuning, experiment tracking, and resource management.
A toolkit and library for developing, evaluating, and reproducing reinforcement learning algorithms.
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