How machine learning ops works with GitLab and continuous machine learning

Continuous integration (CI) is a crucial practice in software development that accelerates development cycles, but it presents unique challenges in machine learning (ML) projects due to the difficulty of managing large datasets and models with traditional version control systems like Git. Iterative.ai has developed an open-source project called Continuous Machine Learning (CML) to address these challenges by adapting powerful CI systems such as GitLab CI for common data science and ML use cases. CML enables automatic model training, testing, and detailed reporting, incorporating data visualizations and metrics in merge requests to facilitate the review of datasets and models. The integration of CML with Data Version Control (DVC) allows tracking of dataset changes alongside code changes, providing a comprehensive CI solution for ML projects. This framework helps to automate tasks, test frequently, and provide fast feedback, thereby expediting project development and increasing visibility within teams.

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