We’ve expanded our SCM integrations to include GitLab, Bitbucket Cloud, and Bitbucket Server so you can implement best practices for machine learning across the lifecycle.

Animated gif showing the addition of GitLab and Bitbucket to our source code options

Introducing GitLab, Bitbucket Cloud, and Bitbucket Server source code management for Algorithmia

Whether you have two models running in production or 200, it can quickly become difficult to manage your code base, collaborate across your team and company, and ensure there are proper security and governance controls across your entire machine learning operations (MLOps) pipeline. That’s why we’re excited to announce that we’ve expanded our source code management (SCM) integrations for Algorithmia Enterprise to include GitLab, Bitbucket Cloud, and Bitbucket Server.

At Algorithmia, we’re committed to integrating with your existing tech stack for AI and machine learning development, so your team can focus on doing your best work in the environment that works for you. Algorithmia already provides a centralized repository for algorithms in production that are backed by our internal Git server and served via our REST API. We focus on providing flexibility in where you store your source code, so we also integrate with GitHub Enterprise SCM. Now, with the new GitLab, Bitbucket Cloud, and Bitbucket Server integrations, you’ll have even more options for integrating with the tools that work for you—ultimately providing you with a complete, single source of truth for your code base.

With the new integrations, you can take advantage of all the enhanced governance and management features that both GitLab and Bitbucket Cloud’s CI/CD workflows offer, while doing your development work in the SCM system that you’re most comfortable using.

Engineering best practices for ML governance

Source code management is a key component of effective ML governance. Using an SCM system is important to ensure the code being developed and deployed into production has been peer-reviewed and versioned, and that any dependencies are documented. In your MLOps pipeline, you’ll also want features that provide fine-grained access controls restricting who can contribute and publish code to production. To help organize and automate the development process, teams need to implement engineering best practices such as using version control, storing source code in a centralized repository, and tracking and understanding any changes that get made. Mature ML pipelines also include support for CI/CD workflows and model management in the deployment and monitoring stages of the lifecycle—and SCM systems are a key component of this. 

Algorithmia’s new SCM integrations help teams and enterprises implement these best practices for machine learning, just like they would for standard software development, and support an effective governance strategy.

Enhance your model management through source code management tools

By connecting your Algorithmia account with your GitLab, Bitbucket Cloud, or Bitbucket Server account, you can now store your source code in your SCM system of choice and deploy it to an algorithm in production on Algorithmia. Utilizing the SCM system your team or company is most familiar with, multiple users can now easily contribute to the same code base, collaborate on a centralized code base, and ensure code quality with best practices like code reviews through pull requests and issues tracking. Also, by integrating these tools with Algorithmia, you can take advantage of Algorithmia’s MLOps platform to manage all stages of your production ML lifecycle within existing operational processes, all while providing advanced security and governance.

Get started with source code management in Algorithmia

The new integrations with GitLab, Bitbucket Cloud, and Bitbucket Server are available now in all Algorithmia Enterprise subscriptions. With these new SCM integrations, it’s now even easier to get started with MLOps—allowing your organization to unlock the value in your machine learning while following ML model management best practices across the lifecycle.

As the enterprise MLOps platform, Algorithmia manages all stages of the production ML lifecycle within existing operational processes, so you can put models into production quickly, securely, and cost-effectively. If you’re not currently using Algorithmia Enterprise, watch a demo today to learn why it provides the quickest time to value for enterprise ML.

What’s next? We’re constantly working on more integrations, including other version control systems and continuous integration pipelines that will enable our users to manage their code bases and deployments seamlessly with Algorithmia. Stay tuned for these and other new features that enhance your organization’s ability to deploy, operate, govern, and secure your machine learning pipelines.

Stephanie Kim