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Learn more about GitLab Duo. AI-powered workflows boost efficiency and reduce cycle times with the help of AI in every phase of the software development lifecycle.
AI-powered workflows
The AI-powered stage consists of the following product led groups:
We're still in the early days of integration AI/ML technologies within GitLab. However we've made much progress in 2023 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 the AI/ML topic of our blog.
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 2021 DevSecOps survey, 75% of respondents report their DevOps teams are either using or planning to use ML/artificial intelligence for testing and code review. Additionally, a majority (55%) of operations teams report their life cycles were either completely or mostly automated. 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: 2023-08-17
Last Updated: 2023-08-17