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GitLab is embracing AI/ML technologies within the software development lifecycle, as the most comprehensive AI-powered DevSecOps platform. Since mid 2021 we've been leverage AI/ML technologies to enrich features on our platform. We're calling these AI-powered capabilities, GitLab Duo.
The name GitLab Duo is rooted in You + GitLab AI = the AI dynamic duo. GitLab Duo goes beyond just being an AI pair programmer: It is an expanding toolbox of features integrated into the DevSecOps Platform to help teams across the entire software development environment become more efficient.
Duo improves team collaboration and reduces the security and compliance risks of AI adoption by bringing the entire software development lifecycle into a single AI-powered application. At GitLab, we believe everyone can contribute. By bringing GitLab Duo capabilities to every persona who uses GitLab, everyone can benefit from AI-powered workflows and organizations can ship secure software faster.
As we continue building GitLab Duo, there are some high level goals that drive our development process.
AI-powered workflows
The AI-powered stage consists of the following product led groups:
We're actively building many AI-powered features into GitLab Duo, some of our upcoming milestones include:
We've made a lot of progress in 2023, introducing GitLab Duo and are quickly building to keep up with the rapid innovations in the wider AI/ML market.
You can keep up with our latest releases by following our AI Features documentation
Major achievements include:
GitLab acquires UnReview as it looks to bring more ML tools to its platform
Integrating UnReview’s technology into the GitLab platform marks our first step in building GitLab’s AI Assisted features for DevOps.
We are currently actively working on an ML model that automatically labels GitLab internal issues based on issue content. You'll see GitLab issues with the automation:ml label that have been automatically labeled by our model. You can also provide training feedback to the model if it is incorrect by applying the automation:ml wrong label. GitLab team members can view a feed of these issues with probability data in Slack in the #feed-tanuki-stan channel.
We pursued this feature first as a way to get a data science workload working within GitLab's existing CI/CD as well as running on top of production GitLab data and interacting with the GitLab data model. This will set the foundation for work in our MLOps group and our other AI Assisted categories listed above.
Based on GitLab’s 2023 What's Next in DevSecOps survey,
These statistics validate the importance of GitLab’s AI Assisted features for DevOps, and integrating automation and machine learning technology like UnReview into the GitLab platform.
Industry analyst research into successful operationalization of machine learning outlines the many challenges organizations face by adopting point solution technologies. This is contrasted with the business value provided by integrating AI Assisted features, DataOps, MLOps, and ModelOps into existing DevOps processes.
"With the rapid increase in cloud adoption, spurred by the COVID-19 pandemic, we’re seeing increased demand for cloud-enabled DevOps solutions," said Jim Mercer, research director DevOps and DevSecOps at IDC. "DevOps teams who can capitalize on cloud solutions that provide innovative technologies, such as machine learning, to remove friction from the DevOps pipeline while optimizing developer productivity are better positioned to improve code quality and security driving improved business outcomes."
GitLab team members can learn more in our internal handbook:
Last Reviewed: 2024-01-30
Last Updated: 2024-01-30