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Machine Learning and Artificial Intelligence promise smarter and powerful software and features. With the depth, breadth, and scale of the GitLab platform, there are many opportunities to leverage data science to make existing GitLab features more intelligent.
Watch a video overview of the GitLab ModelOps direction
This group is focused on how to extend GitLab functionality to provide additional value by leveraging ML/AI. This group will build on existing successful GitLab categories and features to make them smarter, easier to use, and more intelligent.
Integrating UnReview’s technology into the GitLab platform marks our first step in building GitLab’s Applied Machine Learning 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. Internal employees 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 Applied ML categories listed above.
You can read more about our engineering team and our processes in the Applied ML Engineering handbook.
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 Applied Machine Learning for DevOps, 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 applied machine learning, 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."
Signal detection is very hard in a noisy environment. GitLab intends to use ML to warn users of any signals that stand out against the background noise in several features:
Automatically categorizing and labelling is risky. Modern models tend to overfit, e.g. resulting in issues with too many labels. However, similar models can be used very well in combination with human interaction in the form of recommendation engines.
Because of their great ability to recognize patterns, neural networks are an excellent tool to help with scaling by anticipating needs. In GitLab, we can imagine:
Similar to DeepScan.
Identifying anomalous activity within audit events systems can be both challenging and valuable. This identification is difficult because audit events are raw, objective data points and must be interpreted against an organization's policies. Knowing about anomalous behavior is valuable because it can allow GitLab administrators and group owners to proactively manage undesirable events.
This is a difficult problem to solve, but can help to drastically reduce the overhead of managing risk within a GitLab environment.
Last Reviewed: 2021-06-03
Last Updated: 2021-06-03