JFrog Integration Extends MLflow Functionality

Software supply chain platform provider JFrog has announced a new machine learning (ML) lifecycle integration with MLflow, the open-source platform created by Databricks.

This integration, building on earlier native integrations with  Qwak and Amazon SageMaker, goes a long way toward consolidating JFrog's footprint in AI solutions. The new JFrog MLflow plugin is available now on GitHub.

MLFlow is a platform managing the end-to-end machine learning lifecycle. Popular among MLOps teams and data scientists, the platform is helps to stream the intricate process of tracking and managing ML experiments, a feature that facilitates greater collaboration among developers.

The JFrog MLflow plugin extends MLflow functionality by replacing the default artifacts' location with JFrog Artifactory. Once the artifacts are available within JFrog Artifactory, they become an integral part of the company's release lifecycle, just as any other artifact, and are also covered by all the security tools provided through the JFrog platform.

By merging MLflow's capabilities with existing DevOps workflows, JFrog effectively empowers engineers and developers to use a wide array of programming languages within a trusted environment that also enforces compliance and shields against security threats. If offers ML engineers and developers working in Python, Java, and R the flexibility to utilize Artifactory as their model registry.

In fact, this latest integration positions JFrog Artifactory as a universal model registry, streamlining the process for users to develop, manage, and distribute machine learning models and applications powered by generative AI.

"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