Introducing Panels: Custom Visualizations for Machine Learning
In the last three years since Comet was founded, our users and customers trained millions of models on anything from…
Investing in AI: Unlocking Profitable Machine Learning with Experiment Management
This post was originally published as a sponsored post by Dell Technologies and Intel on CIO.com. We live in an…
comet.ml announces $4.5 million investment to double-down on Comet Enterprise and Meta Learning Capabilities
Current customers include Ancestry, General Electric, The National Institute of Health and Fortune 100 Companies NEW YORK, NY--comet.ml, the industry-leading…
Predictive Early Stopping – A Meta Learning Approach
Introduction Model training is arguably the most time consuming, and computationally demanding part of the Machine Learning pipeline. Depending on…
New Integration: Comet + PyTorch Lightning
Machine learning practitioners can now use PyTorch Lightning with Comet to speed up research cycles and build better models, faster.…
How to Make Remote Work Effective for Data Science Teams
This article was written in collaboration with Tyler Folkman, Head of AI at Branded Entertainment Network. To read more of…
Why software engineering processes and tools don’t work for machine learning
While AI may be the new electricity significant challenges remain to realize AI potential. Here we examine why data scientists and teams can’t rely on software engineering tools and processes for machine learning.
