MLOps Vendor Integrates Machine Learning Platform with GitHub

Algorithmia today announces a new integration of its namesake machine learning (ML) and operations platform with GitHub. The integration will allow users to store their code on GitHub and deploy it directly to the platform, which makes it easier for a team working on an ML project to contribute and collaborate to a centralized code base.

"As more applications are driven by machine learning, it will become increasingly important for these projects to be part of an organization's software development lifecycle," said Diego Oppenheimer, CEO of Algorithmia, in a statement. "Model deployment and management should be as simple as checking-in any other code. With Algorithmia's source code management integrations, it will be. Data scientists will be as agile as any software team."

Algorithmia bills itself as an MLOps solution provider. The Seattle-based company's platform is designed to allow data scientists and ML engineers to automate the DevOps, management, and deployment of AI/ML models in the language they choose. It enables users to connect data sources, orchestration engines, and step functions, and deploy models from major frameworks, languages, platforms, and tools.

This new integration promotes machine learning initiatives into organizations' software development life cycle. Users will have the benefits of a centralized code repository: increased ML portability, compliance, and security for enterprise ML projects. The integration adheres to DevOps best practices for managing ML models in production, the company says, which should shorten the time-to-value for data science initiatives.

Users of the Algorithmia platform can also take advantage of GitHub's governance applications around dependency management to ensure their models aren't at risk of utilizing package versions with deprecated features.

A study of 500 data scientists, commissioned by Algorithmia, found that they spend up to 75% of their time with non-data science tasks—things like cleaning and prepping data and building data pipelines. The study also found that 60% of machine learning projects were failing at the deployment stage. Both of those statistics are evidence of a growing need for better data science tools, Oppenheimer said.

The GitHub integration is now available to public users and for existing Algorithmia Enterprise customers.

About the Author

John K. Waters is the editor in chief of a number of sites, with a focus on high-end development, AI and future tech. He's been writing about cutting-edge technologies and culture of Silicon Valley for more than two decades, and he's written more than a dozen books. He also co-scripted the documentary film Silicon Valley: A 100 Year Renaissance, which aired on PBS.  He can be reached at