{"id":8090,"date":"2023-11-02T10:16:15","date_gmt":"2023-11-02T18:16:15","guid":{"rendered":"https:\/\/live-cometml.pantheonsite.io\/?p=8090"},"modified":"2025-04-24T17:04:44","modified_gmt":"2025-04-24T17:04:44","slug":"streamline-ml-model-development-with-gitlabs-devops-platform-and-comet","status":"publish","type":"post","link":"https:\/\/www.comet.com\/site\/blog\/streamline-ml-model-development-with-gitlabs-devops-platform-and-comet\/","title":{"rendered":"Streamline ML Model Development with GitLab\u2019s DevOps Platform and Comet"},"content":{"rendered":"\n\n\n\n\n
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Editor\u2019s Note: This blog post was co-written alongside William Arias<\/a>, Technical Marketing Manager at GitLab, and has been syndicated with permission. Check out the original version of this post on GitLab\u2019s blog.<\/a><\/p><\/blockquote>\n

Introduction<\/strong><\/h1>\n

Building ML-powered applications comes with numerous challenges. When we talk about these challenges, there is a tendency to overly focus on problems related to the quality of a model\u2019s predictions \u2014 things like data drift, changes in model architectures, or inference latency.<\/p>\n

While these are all problems worthy of deep consideration, an often overlooked challenge in ML development is the process of integrating a model into an existing software application.<\/p>\n

If you\u2019re tasked with adding an ML feature to a product, you will almost certainly run into an existing codebase that must play nicely with your model. This is, to put it mildly, not an easy task.<\/p>\n

ML is a highly iterative discipline. Teams often make many changes to their codebase and pipelines in the process of developing a model. Coupling an ML codebase to an application\u2019s dependencies, unit tests, and CI\/CD pipelines will significantly reduce the velocity with which ML teams can deliver on a solution, since each change would require running these downstream dependencies before a merge can be approved.<\/p>\n

In this post, we\u2019re going to demonstrate how you can use Comet<\/a> with GitLab\u2019s DevOps platform to streamline the workflow for your ML and Software Engineering teams, allowing them to collaborate without getting in each other\u2019s way.<\/p>\n<\/div>\n<\/div>\n<\/div>\n\n\n\n

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TL;DR \u2014 Check out this technical walkthrough for an inside look at how you can add reliable CI\/CD pipelines to your ML workflows with GitLab + Comet.<\/p><\/blockquote>\n

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