From a2cb55804e3415992e057a5c985e470733246c32 Mon Sep 17 00:00:00 2001 From: Vincent Koc Date: Wed, 5 Mar 2025 05:06:58 +1100 Subject: [PATCH] Added Comet's Opik --- README.md | 1 + 1 file changed, 1 insertion(+) diff --git a/README.md b/README.md index 4d5d1a0..28161a5 100644 --- a/README.md +++ b/README.md @@ -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.