awesome/machine-learning
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Added Comet's Opik

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Vincent Koc 2025-03-05 05:06:58 +11:00 • committed by GitHub
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@ -231,6 +231,7 @@ Further resources:
* [MXNet](https://github.com/apache/incubator-mxnet) - Lightweight, Portable, Flexible Distributed/Mobile Deep Learning with Dynamic, Mutation-aware Dataflow Dep Scheduler; for Python, R, Julia, Go, JavaScript and more.
* [N2D2](https://github.com/CEA-LIST/N2D2) - CEA-List's CAD framework for designing and simulating Deep Neural Network, and building full DNN-based applications on embedded platforms
* [oneDNN](https://github.com/oneapi-src/oneDNN) - An open-source cross-platform performance library for deep learning applications.
* [Opik](https://www.comet.com/site/products/opik/) - Open source engineering platform to debug, evaluate, and monitor your LLM applications, RAG systems, and agentic workflows with comprehensive tracing, automated evaluations, and production-ready dashboards. ([Source Code](https://github.com/comet-ml/opik/))
* [ParaMonte](https://github.com/cdslaborg/paramonte) - A general-purpose library with C/C++ interface for Bayesian data analysis and visualization via serial/parallel Monte Carlo and MCMC simulations. Documentation can be found [here](https://www.cdslab.org/paramonte/).
* [proNet-core](https://github.com/cnclabs/proNet-core) - A general-purpose network embedding framework: pair-wise representations optimization Network Edit.
* [PyCaret](https://github.com/pycaret/pycaret) - An open-source, low-code machine learning library in Python that automates machine learning workflows.