{"id":31,"date":"2020-12-03T09:18:32","date_gmt":"2020-12-03T17:18:32","guid":{"rendered":"https:\/\/live-cometml.pantheonsite.io\/?post_type=press_release&p=31"},"modified":"2020-12-03T09:18:32","modified_gmt":"2020-12-03T17:18:32","slug":"comet-ml-debuts-collaborative-workspaces-for-data-science-and-mlops-teams","status":"publish","type":"press_release","link":"https:\/\/www.comet.com\/site\/about-us\/news\/press-releases\/comet-ml-debuts-collaborative-workspaces-for-data-science-and-mlops-teams\/","title":{"rendered":"Comet ML Debuts Collaborative Workspaces for Data Science and MLOps Teams"},"content":{"rendered":"

Comet ML<\/a>, a leading provider of machine learning operations (MLOps) solutions that accelerate getting machine learning models into production, today announced updates to Comet Workspaces, including the introduction of Interactive Reports, ML Templates and the industry\u2019s first workflow for proactively considering carbon emissions as part of the machine learning process. Today\u2019s updates further empower data scientists and teams to build better models faster, while ensuring that organizations can continue to operate in an environmentally responsible manner.<\/p>\n

One of the most pressing challenges facing machine learning and artificial intelligence teams today is the difficulty of delivering quality, trained models from experiment to production. Recent studies have shown that as many as\u00a055 percent of companies never take their models to production<\/a>, and\u00a0nearly 87 percent of machine learning projects fail<\/a>. Despite having cutting-edge technologies to build machine learning models, tools that enable enterprise machine learning teams to implement a consistent MLOps process, workflows and reporting have lagged behind.<\/p>\n

\u201cWhile much has been said about the potential of AI and machine learning for business, a majority of that innovation hasn\u2019t translated into value yet,\u201d said Gideon Mendels, Co-founder and CEO, Comet. \u201cThe challenges range from lack of defined workflow and processes to inability to collaborate and share insights across teams. That\u2019s why MLOps has arisen as a key concept\u2014defining the people, processes and technologies that will drive wide-spread success with machine learning and AI at scale. But this must also be done responsibly, in a way that considers and addresses significant computing requirements and emissions.\u201d<\/p>\n

Comet Enterprise automates experiment and model management, automatically tracking data sets, code changes, experimentation history, and models all at scale. One key component is Comet Workspaces. Since its inception, Comet Workspaces has provided a one-stop-shop for data science and machine learning teams to consolidate, control, and collaborate on machine learning projects and experiments.<\/p>\n

With additions of Interactive Reports, ML Templates, and the CodeCarbon Panel, Comet Workspaces deliver an integrated approach to managing ML teams and development\u2014from planning to delivering to reporting the status and results of machine learning projects\u2014all within the context of environmental impact.<\/p>\n

\u201cCodeCarbon is an open source tool that estimates the amount of carbon dioxide (CO2) produced by computing resources both locally and on the cloud,\u201d said Sasha Luccioni, postdoctoral researcher at Mila. \u201cComet ML has been a great partner as we\u2019ve worked together to help\u00a0 researchers and developers understand and reduce emissions. With Comet ML\u2019s new CodeCarbon Panel and workflow, developers will be able to incorporate those decisions directly into the experiment and model training process.\u201d<\/p>\n

Today\u2019s updates include:<\/p>\n