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The Data Science Section focuses on transforming data into useful insights and actions.
The Data Science section is comprised of two stages:
The work of the Data Science section cuts across the entire Gitlab DevOps platform, from our reliance on features like source code management (SCM) and CI/CD to support machine learning (ML) and artificial intelligence (AI) workloads to how we enhance platform features with ML/AI to make them more intelligent and automated. The section is unique in that the value it creates slices horizontally across all other GitLab sections and stages, providing a holistic approach to data science use cases across the software development lifecycle.
Both ModelOps and Anti-abuse are components of GitLab's Data Science product strategy. ModelOps focuses on enabling Data Scientists to use GitLab effectively. Anti-Abuse will use Data Science techniques to build a user activity data system and automation to protect GitLab from abuse and misuse. Initially, Anti-abuse's work is focused on stabilizing GitLab from abuse, but it will also build new revenue-generating products related to Insider Threat detection and UEBA tooling, both of which will rely on data science techniques.
This section aligns cross-functional teams and organizational structures across Product, Engineering, UX, and technical writing teams. This streamlines the management chain of all individuals across functions as well as aligns unique product development areas of focus and challenges. Both the ModelOps and Anti-Abuse stages share some unique properties that other Gitlab sections/stages do not:
Last Reviewed: 2022-08-24
Last Updated: 2022-08-24