Is Comet the new Github for Artificial Intelligence?

2 min read

Comet.ml, is one of the infrastructure-agnostic machine learning (ML) platforms which is simple, fast and free for open source projects. It launched the first platform for data science and machine learning users to track, monitor and optimize their machine learning models. Comet allows data science teams to track their code, experiments, and results on machine learning projects.

The newly launched platform allows users to optimize their machine learning and artificial intelligence models and twist hyperparameters of their demonstrations. The platform also provides dashboards which help in collaboration of codes of the ML research and results. It allows researchers to view results with an intuitive graph and compare various aspects and versions of the machine learning experiments.

Comet also functions on popular Machine Learning libraries such as Keras, TensorFlow, PyTorch, scikit-learn, and Theano. The platform allows teammates to collaborate real-time without affecting the mobility and adaptability of the datasets and production models.

Key Features of Comet:

Single-line Tracking – Start tracking with just a single line into your training code. It works on any machine and with any type of model.
Compare Experiments – Compare different experiments and observe the code differences, hyper-parameters, and various other data points.
Integration with Git – Comet allows to integrate with Github and other git service providers. After finalizing the experiment, it automatically generates a pull request with the model with the best accuracy to the Github repository.
Collaboration – Share multiple projects with team members and stakeholders along with visibility and insights into project team performance.
Documentation – Provides Notes section allowing you to add and manage documentation for all projects and training experiments.

Comet is already adopted by more than 30 industry leaders and research universities with more than 6000 large-scale machine learning models.

Check out the video to know more about the platform functionality: