JFrog announces MLflow integration to power machine learning model management

JFrog Ltd, a software supply chain company, today announced a new machine learning lifecycle integration between JFrog Artifactory and MLflow, an open source software platform originally developed by Databricks Inc.

The new integration is designed to give JFrog users a way to build, manage, and deliver apps that leverage machine learning models and generative artificial intelligence, along with other software development components, in a streamlined end-to-end DevSecOps workflow. I am. This integration allows enterprises to verify the security and provenance of machine learning models and ensure responsible AI practices.

This integration addresses the issue where more than 80% of machine learning models built to create new AI-powered applications fail to deploy due to technical issues integrating the models into existing operations. aims to address. JFrog and MLflow integration helps organizations overcome challenges by integrating MLflow's model development solutions with existing mature DevOps workflows for end-to-end visibility, automation, control, and traceability. Masu.

“To expand the ability of organizations to deploy and successfully deliver AI and generative AI-powered applications, developers and data science teams need to manage trust, the same way they manage other packages. It's important to be able to manage your models with confidence,” said Yoav Landman, Chief Technology Officer. Officer of JFrog. “This includes the ability to use a single, universal and scalable system of record for all binaries, control versioning, apply security checks, and control the lifecycle of your models. .”

Building on previous successful integrations between JFrog, Amazon SageMaker, and Qwak AI Ltd., the combination of JFrog Artifactory and MLflow allows machine learning engineers and Python, Java, and R developers to use Artifactory as a model registry. Now you have the freedom to work with your preferred tool stack. .

In addition, JFrog's platform proxies Hugging Face, giving developers easy access to available open source models while monitoring malicious models to enforce license compliance. The solution also comes with software security features and scanners provided by the JFrog platform to maintain risk-free machine learning applications.

The ability to detect malicious models is also an important feature as their number begins to grow rapidly. In February, the JFrog Security Research team discovered hundreds of malicious AI models in the Hugging Face AI repository that pose a significant risk of data breaches and attacks.

Photo: JFrog