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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 DataOps and MLOps, we will be putting that experience into practice with the stage's other groups:
Watch Sr. Director of Product David DeSanto, Engineering Manager Monmayuri Ray, 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.
Commit Virtual 2021: What the ML is up with DevOps and AI? A ModelOps Overview In this session, we will discuss the three pillars of ModelOps, including how to integrate Data Science into DevOps. This will include a brief history on how we got here, as well as where we are going. We will discuss GitLab’s recent acquisition of UnReview and how GitLab plans to leverage ML/AI within our platform to improve user experience, as well as empower users to include ML/AI within their applications. We will conclude with some examples of how customers are empowering their data science workloads with GitLab today.
GitLab ModelOps is currently composed of 4 groups with a variety of open roles we are actively recruiting:
The ModelOps stage also receives support from 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 actively staffing up. We've recently hired an Engineering Manager (Monmayuri Ray) and Product Manager (Taylor McCaslin) and are actively hiring multipule engineering roles (see above). 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.
Personas
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.
Personas
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.
Personas
Last Reviewed: 2021-10-26
Last Updated: 2021-10-26
