JFrog (RSA booth #0455), a liquid software company known for creating the JFrog software supply chain platform, announced new 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 extended its universal AI solutions, offering organizations a single system of record with Artifactory as a model registry. The new integration gives JFrog users a powerful way to build, manage and deliver ML models and GenAI-powered apps, alongside 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.
“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 allows 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. JFrog’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.
The JFrog security research team recently discovered hundreds of instances of malicious AI ML models on the public Hugging Face AI repository, posing a significant risk of data breaches or attacks. This incident highlights the potential threats lurking within AI-powered systems and underscores the need for constant security vigilance and proactive cyber hygiene.
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. Developers interested in going hands-on with these new features can download the free plug-in here.
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