New MLflow Plugin Directs Artifact Storage to JFrog Artifactory

JFrog, a leading software supply chain platform provider, has announced a new machine learning (ML) lifecycle integration with MLflow, the open-source platform from Databricks. This builds on JFrog's prior integrations with Qwak and Amazon SageMaker, further solidifying its presence in AI solution management. You can now find the JFrog MLflow plugin on GitHub.

MLflow is a popular platform for managing the complete ML lifecycle, simplifying the process of tracking and orchestrating ML experiments. It is especially valued by MLOps teams and data scientists for its collaborative tracking features.

The new JFrog plugin for MLflow enhances the platform by directing artifact storage to JFrog Artifactory. The ML artifacts are then managed alongside other software binaries, subject to the same release processes and protected by JFrog's security tools.

This integration melds MLflow's capabilities with standard DevOps workflows, allowing Java, Python, and R developers to engage in a secure, compliance-oriented environment that safeguards against security vulnerabilities.

JFrog Artifactory now also serves as a universal model registry, making the development, management, and deployment of machine learning models and GenAI-powered applications more seamless.

"For organizations to successfully embrace and deliver AI and GenAI–powered applications at scale, developers and data science teams must manage models with trust, the same way they manage all software packages," said JFrog CTO Yoav Landman, in a statement. "This is only possible using a universal, scalable, single system of record for all binaries that delivers versioning, lifecycle, and security controls, which our new integration with MLflow provides."

Additionally, JFrog's platform acts as a proxy to Hugging Face, simplifying access to open-source models while also screening for malicious models and ensuring license compliance. The platform also incorporates JFrog's security features and scanners to uphold the integrity of machine learning applications.

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 [email protected].