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| Stage | ModelOps |
| Maturity | minimal |
| Content Last Reviewed | 2024-04-25 |
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 experiments, models and versions, and deployment.
The Model Registry is the foundational capability for MLOps within GitLab. We plan to release the capability as Beta in FY25'Q2. Beta will include the ability to perform CRUD operations directly in the UI.
We currently have an experimental Model experiments feature that is integrated with MLFlow. We are currently evaluating how to iterate on the next step that brings utility to our user while also enabling us to build towards the future.
A model repository is made more useful by adding the ability to deploy to production. With deployment, users can collaborate and complete a full basic ML workflow within GitLab.
Data Scientist are not developers, in the traditional sense. They primarily work in notebooks. We want to make it easy for Data Scientist to store the models they're working on in the model registry by offering integration with popular tools like Google Collab.
