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Stage | ModelOps |
Maturity | minimal |
Content Last Reviewed | 2024-09-20 |
Machine Learning Operations (MLOps) aims to bring together data exploration, experimentation, evaluation, deployment, management, and automation of machine learning models in production reliably and efficiently.
The concept map above shows the key activities of MLOps, mapped to the user personas, and mapped to the tools and capabilities that help users accomplish those activities.
You can further explore this MLOps concept map (along with DataOps and LLMOps).
or watch the accompanying overview video.
Data Scientist, ML Engineers, and stakeholders work together in GitLab to experiment, evaluate, verify, deploy, monitor and keep models secure and up-to-date. Their processes are reproducible, automated, collaborative, scalable, and monitored.
They further collaborate with other product development teams in GitLab so that there is tight coordination between models and their dependent applications. Teams stay informed on status of various components and can seamlessly coordinate making changes to production systems.
Like software development, machine learning and model development benefit from automation and collaboration to consistently and iteratively deliver value. As machine learning becomes more prevalent, the number of individuals, roles, and frequency of changes increases. This causes friction and often results in costly errors. Instead of maintaining siloed workflows, bringing ML workflows into GitLab as a single collaboration platform extends the DevOps culture to data scientists, helping organizations achieve better results.
Over the course of FY25, GitLab is ramping up a team dedicated to MLOps. We will focus on the core of managing models and their versions, along with developing a MVC of model deployment.