Comet ML Debuts Collaborative Workspaces For Data Science And MLOps Teams
Comet ML, 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’s first workflow for proactively considering carbon emissions as part of the machine learning process. Today’s 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.
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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 55 percent of companies never take their models to production, and nearly 87 percent of machine learning projects fail. 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.
“While much has been said about the potential of AI and machine learning for business, a majority of that innovation hasn’t translated into value yet,” said Gideon Mendels, Co-founder and CEO, Comet. “The challenges range from lack of defined workflow and processes to inability to collaborate and share insights across teams. That’s why MLOps has arisen as a key concept—defining 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.”
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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.
With additions of Interactive Reports, ML Templates, and the CodeCarbon Panel, Comet Workspaces deliver an integrated approach to managing ML teams and development—from planning to delivering to reporting the status and results of machine learning projects—all within the context of environmental impact.
“CodeCarbon is an open source tool that estimates the amount of carbon dioxide (CO2) produced by computing resources both locally and on the cloud,” said Sasha Luccioni, postdoctoral researcher at Mila. “Comet ML has been a great partner as we’ve worked together to help researchers and developers understand and reduce emissions. With Comet ML’s new CodeCarbon Panel and workflow, developers will be able to incorporate those decisions directly into the experiment and model training process.”
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