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Comet ML Debuts Collaborative Workspaces for Data Science and MLOps Teams

Innovations in Comet Workspaces deliver Interactive Reports, Templates and the industry’s first workflow to consider environmental impact during the machine learning process

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.

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.”

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.”

Today’s updates include:

  • Interactive Reports — share and report the results of your experiments internally or externally via an intuitive and fully interactive user interface (UI) which supports fully customizable code panels. Visit the Report Library to see interactive reports in action. 
  • ML Templates — use pre-built interactive templates that accelerate planning and reporting for the most machine learning common needs – such as project initiation and business stakeholder reports. See the templates.
  • CodeCarbon Emissions Panel — use this interactive panel to proactively incorporate and consider the carbon emissions of your projects, ensuring that models can be optimized while being environmentally responsible. See CodeCarbon Interactive Report here.

“Our goal at Comet is to solve the challenges that organizations face when getting from experimentation to production,” continued Mendels. “The biggest challenge AI teams have today isn’t DevOps or infrastructure but actually building models that meet the business KPI. That’s why our focus is on making experimentation and model management streamlined and predictable as you go through the research process. The more automated and intuitive you can make the process by delivering thoughtful and pre-built tools for data scientists, the more likely it becomes that organizations can drive real value with machine learning and AI.”

The CodeCarbon Panel was made possible by CodeCarbon, a joint initiative between MilaBCG GAMMAHaverford College, and Comet ML. You can learn more about the initiative in the and its outcomes in this recent press release and at

Founded in 2017, Comet is headquartered in New York, NY. Comet is free to try and for academics, with startup, team, and enterprise licensing available. Learn more at


About Comet

Comet provides a self-hosted and cloud-based MLOps solution that enables data scientists and teams to track, compare, explain and optimize experiments and models. Backed by thousands of users and multiple Fortune 100 companies, Comet provides insights and data to build better, more accurate AI models while improving productivity, collaboration and visibility across teams. Learn more at


About MILA

Founded by Professor Yoshua Bengio of the Université de Montréal, Mila is a research institute in artificial intelligence which rallies over 700 researchers specializing in the field of deep learning. Based in Montreal, Mila’s mission is to be a global pole for scientific advances that inspires innovation and the development of AI for the benefit of all. Mila is a non-profit organization recognized globally for its significant contributions to the field of deep learning, particularly in the areas of language modelling, machine translation, object recognition and generative models. For more information, visit

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