Building GitLab with GitLab: Why there is no MLOps without DevSecOps

GitLab's Data Science team is enhancing its predictive model workflows by incorporating DevSecOps practices to address common challenges such as error-prone manual steps, difficulty in replicating experimental results, and lengthy machine learning model training times. By utilizing the GitLab DevSecOps Platform, the team automates and standardizes various stages of their data science pipelines, including building a common container image for reproducibility, automating model training with GPU-enabled CI/CD, and leveraging experiment tracking for metadata and artifact storage. The use of GPU hardware significantly speeds up model training, while the GitLab Container Registry ensures efficient management of dependencies. These practices not only streamline the development process but also lay the groundwork for future MLOps advancements, which will include enhanced security features and workflow monitoring. The initiative is part of GitLab's broader strategy of "dogfooding" its platform capabilities to improve its own development processes.

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