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Enable and empower data science workloads on GitLab
GitLab ModelOps aims to bring data science into Gitlab both within existing features to make them smarter and more intelligent, but also empowering Gitlab customers to build and integrate data science workloads within GitLab.
The ModelOps Stage is currently outside of the GitLab DevOps lifecycle. We believe that data science features can span across all DevOps stages, making existing features more intelligent and automated.
There are two areas of relevance to GitLab ModelOps which we believe are critical to having end to end functioning data science workloads on GitLab:
With our learnings about building and deploying data science workloads with MLOps, we will be putting that experience into practice with our Applied ML group. This group will focus on infusing data science into existing GitLab features.
Watch Sr. Director of Product David DeSanto and Principal Product Manager Taylor McCaslin discuss an overview of the Gitlab ModelOps stage, dig into the focus for each group, and answer common questions about this new area of investment for GitLab.
GitLab ModelOps is currently composed of two experimental single-engineer groups, MLOps and DataOps, as well as a collection of several team members, from across different GitLab departments, dedicating approximately 20% of their time enabling data science workloads for existing GitLab Stages and Categories by enabling data science workloads. Our goal is to provide a blueprint to where GitLab should fund full teams to bring data science into GitLab features where relevant to empower customer's data science workloads. To learn more about GitLab’s investment areas, please visit the Product Investments section of the GitLab Handbook.
Today, the ModelOps Stage is not a fully staffed team and is supported by several team members who dedicate approximately 20% of their time to move our goals forward. With this said, there has been significant progress, including the following:
GitLab identifies who our DevSecOps application is built for utilizing the following categorization. We list our view of who we will support when in priority order:
To capitalize on the potential opportunities, the ModelOps Stage has features that make it useful to the following personas today:
Data Science workloads can be complicated and can leverage specialized hardware and development environments not common to traditional software development teams. The ModelOps stage is focused on the intersection of data scientists exploring models and feature development and the developers who must then deploy those data science features into production.
Data scientists have unique roles within organizations. They are more scientists than developers, following hypotheses and data to explore models and develop data science-powered features.
We aim to serve data scientists as they balance art and science within software engineering teams. Data scientists wear a lot of hats to get from hypothesis to data science feature that generates value. GitLab is not a tool of choice for data scientists and we aim to change that by making it easy to configure, build, and execute data science feature development within GitLab.
The larger the organization, the harder it is for security teams to stay on top of everything happening in complex, ever-changing environments. As an organization's source code management and DevSecOps platform, GitLab holds a lot of sensitive, high-value data. We want to help security teams secure that data. This is a job to which automated data science features can be well suited, including monitoring high-value assets around the clock.
Last Reviewed: 2021-06-03
Last Updated: 2021-06-03