JFrog Integrates with DataBricks-Developed MLflow to Provide Streamlined Machine Learning Lifecycle

JFrog Ltd. the Liquid Software company and creators of the JFrog Software Supply Chain Platform, is offering a new machine learning (ML) lifecycle integration between JFrog Artifactory and MLflow, an open source software platform originally developed by Databricks. 

Following native integrations released earlier this year with Qwak and Amazon SageMaker, JFrog extends their universal AI solutions, offering organizations a single system of record with Artifactory as a model registry. 

According to the company, the new integration gives JFrog users a powerful way to build, manage, and deliver ML models and generative AI (GenAI)-powered apps alongside all other software development components in a streamlined, end-to-end, DevSecOps workflow. By making each model immutable and traceable, companies can validate the security and provenance of ML models, enabling responsible AI practices.

 JFrog’s integration with MLflow unites the MLflow popular open source model development solution with an organization’s mature DevOps workflows—delivering end-to-end visibility, automation, control, and traceability of ML models from experimentation to production.

“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 Yoav Landman, CTO, JFrog. “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.”

Building on its successful integrations with all major ML tools in the market, the combination of JFrog Artifactory and MLflow enables ML engineers, Python, Java, and R developers with the freedom to work with their preferred tool stack, using Artifactory as their gold-standard model registry.

Frog’s universal, scalable platform also natively proxies Hugging Face allowing developers to always access available open source models while simultaneously detecting malicious models and enforcing license compliance. The solution also comes with the software security features and scanners provided by the JFrog Platform to maintain risk-free ML applications.

Uniting JFrog Artifactory with MLflow will empower users to more easily build, train, and deploy models with greater security, governance, versioning, traceability, and trust by leveraging  JFrog’s scanning environment to rigorously examine every new model uploaded to Hugging Face.

For more information about this news, visit