{"id":13148,"date":"2025-03-13T18:31:16","date_gmt":"2025-03-13T18:31:16","guid":{"rendered":"https:\/\/comet-marketing-site.lndo.site\/?page_id=13148"},"modified":"2025-11-03T20:29:39","modified_gmt":"2025-11-03T20:29:39","slug":"carerev","status":"publish","type":"page","link":"https:\/\/www.comet.com\/site\/customers\/carerev\/","title":{"rendered":"How CareRev Uses Comet to Accelerate AI Innovation"},"content":{"rendered":"\n
California-based healthcare startup CareRev knows machine learning is critical in improving customer experience and driving company growth. They function as a marketplace by connecting healthcare professionals with facilities and patients. The CareRev app allows users to select their preferred shifts and facilities, while healthcare facilities use CareRev as a SaaS to meet staffing demands.<\/p>\n\n\n\n
The team at CareRev has been in a hyper-growth mode over the past few months. Even as a new machine learning team, their Machine Learning Engineers knew they needed an ML platform with a powerful UI to track and compare their experiments. They were looking for a tool to help their ML team scale, reduce costs, and provide data scientists with better insight into the model training process, saving them time. CareRev loves Comet\u2019s efficiency and easy integration with other tools. <\/p>\n\n\n\n
\u201cThe real selling point of Comet was how it could easily integrate with our training code, easily track everything that we needed, and have a good UI to show it when comparing experiments,\u201d says Kes Crandall, ML Engineer at CareRev. After reviewing multiple vendors, the company ultimately decided to purchase Comet instead of another company or hire programmers to build something similar from scratch. \u201cThe cost of building something like Comet would have been incredibly high because it has an easy-to-use UI component and custom visualization features. And also, from a buying standpoint, it was pretty cheap for what the registry and experiment tracker offers. It was a no-brainer,\u201d added Crandall. <\/p>\n\n\n\n
A Central Repository for Faster Iteration<\/h4>\n\n\n\n
The CareRev team initially started with MLflow, an open-source experiment management platform that didn\u2019t meet their needs. \u201cIt was not well maintained, and compared to Comet, it didn\u2019t have all the features needed to track experiments, especially as we started scaling,\u201d said Crandall.<\/p>\n\n\n\n
Doubling back on experiments to see which model performed best can be frustrating. With no versioning or tracking, teams risk expending unnecessary effort on training runs. The CareRev team was looking for a solution that offered MPM, experiment tracking, and a model registry all in one tool to store their experiment results and remove some friction. With a central repository, they could compare results, visualize the data, and reproduce models. Implementing Comet\u2019s platform helped them achieve just that. For instance, Comet\u2019s Artifacts feature helped the CareRev team manage their assets and artifacts for easier reproducibility. \u201cThe assets and artifact section of Comet saved me a lot of time. Previously, whenever we were testing models, we had to go and find all the inputs we added to the model after viewing the model\u2019s performance. But with Comet, they have all that stored for you. You can go to that singular link and grab all the model information you may want rather than going through a bunch of different services elsewhere to find that,\u201d said Michael Fanous, an ML Engineer intern at CareRev.<\/p>\n\n\n\n
Comet\u2019s Features and Benefits<\/h4>\n\n\n\n
Machine learning is a highly iterative process that requires the diligent tracking of multiple sources of variability. Tracking changing variables becomes extremely difficult when dealing with multiple experiments simultaneously. An experiment management tool like Comet makes model development more repeatable and transparent by organizing your entire workflow from start to finish. When asked about their favorite Comet feature, Alec Karfonta, a Senior ML Engineer of CareRev, said that \u201cit\u2019s the big table of all of the experiments where I can select which columns to show up so I can see the different hyperparameters for the model, the different metrics that we want to look at, and just having those all together in one place is a game changer.\u201d Fanous also responded that his favorite Comet feature was \u201cthe tagging system where you can tag different experiments based on different things you\u2019re trying to try out with the model. When you do that, it\u2019s really easy to aggregate the data in a way in which analysis is much simpler versus going run for run and checking every single outcome.\u201d<\/p>\n\n\n\n<\/a>CareRev\u2019s Dashboard<\/figcaption><\/figure>\n\n\n\n<\/a>CareRev\u2019s Column Settings<\/figcaption><\/figure>\n\n\n\n
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