{"id":2657,"date":"2021-11-09T16:30:05","date_gmt":"2021-11-10T00:30:05","guid":{"rendered":"https:\/\/live-cometml.pantheonsite.io\/blog\/streamline-model-development-with-gitlabs-devops-platform-and-comet\/"},"modified":"2021-11-09T16:30:05","modified_gmt":"2021-11-10T00:30:05","slug":"streamline-model-development-with-gitlabs-devops-platform-and-comet","status":"publish","type":"post","link":"https:\/\/www.comet.com\/site\/blog\/streamline-model-development-with-gitlabs-devops-platform-and-comet\/","title":{"rendered":"Streamline Model Development with GitLab’s DevOps Platform and\u00a0Comet"},"content":{"rendered":"\n

Leverage GitLab\u2019s powerful CI\/CD pipelines and Comet\u2019s Experiment Management capabilities to create a model development workflow that enables better collaboration between Application Teams and Machine Learning\u00a0Teams.\u00a0<\/h3>\n\n\n\n

When we talk about the challenges of building ML-powered applications, we tend to focus on problems related to the quality of a model\u2019s predictions\u200a\u2014\u200athings like data drift, changes in model architectures, or inference latency.<\/p>\n\n\n\n

But there’s an often overlooked challenge that plays a vital role in the success or failure of these applications\u2014 the process of integrating a model into existing software.<\/p>\n\n\n\n

ML is a highly iterative discipline. In the process of developing a model, teams often make many changes to their codebase and pipelines. 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\u2014this is because each change will almost assuredly require running these downstream dependencies before a merge can be approved.<\/p>\n\n\n\n

It’s with these unique challenges in mind that we’re thrilled to share our latest integration with GitLab, a powerful, pipeline-based DevOps platform that helps teams better manage their CI\/CD workflows.<\/p>\n\n\n

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About the Integration<\/strong><\/h2>\n\n\n\n

With this integration between the GitLab DevOps platform and Comet, we can keep workflows between ML and Engineering teams separate, while enabling cross-team collaboration\u2014by preserving the visibility and audit-ability of the entire model development process across teams.\u00a0<\/p>\n\n\n\n

Using the combined power of Comet and GitLab, ML and application teams can implement a robust, seamless process where discussions, code reviews, and model performance metrics get automatically published and tracked within Merge Requests on the GitLab DevOps platform.<\/p>\n\n\n\n

With this process in place, teams can increasing the velocity and opportunity for collaboration between data scientists and software engineers for machine learning workflows.<\/p>\n\n\n\n

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Experiment Metrics Displayed in the Merge\u00a0Request<\/a><\/figcaption>\n<\/figure>\n\n\n\n

Getting Started<\/h2>\n\n\n\n

To get started with this powerful integration, we’ve compiled a number of resources:<\/p>\n\n\n\n