JFrog has unveiled a new partnership between JFrog Artifactory and Amazon SageMaker, and the goal of the collaboration is to optimize the overall machine self-learning process. This will allow companies to manage their ML models with the same efficiency and security as other software components in the DevSecOps workflow.

With the new integration, ML models are immutable, traceable and secure. In addition, JFrog has enhanced its ML model management solution with new versioning capabilities, ensuring that compliance and security are an integral part of the ML model development process.

“As more companies begin managing big data in the cloud, DevOps team leaders are asking how they can scale data science and ML capabilities to accelerate software delivery without introducing risk and complexity. The combination of Artifactory and Amazon SageMaker creates a single source of truth that indoctrinates DevSecOps best practices to ML model development in the cloud – delivering flexibility, speed, security, and peace of mind – breaking into a new frontier of MLSecOps”, said Kelly Hartman, SVP of global channels and alliances at JFrog.

A curious fact
According to a Forrester survey, half of data decision makers believe that implementing governance policies within AI/ML is a major challenge to its widespread use, and 45% consider data and model security a key concern.

JFrog has effectively tackled concerns related to ML model management through its integration with Amazon SageMaker, implementing DevSecOps best practices. This integration enables developers and data scientists to expedite ML project development while upholding enterprise-level security and compliance with regulatory and organizational standards, as outlined by JFrog.

Additionally, JFrog has incorporated new versioning features into its ML Model Management solution, complementing the integration with Amazon SageMaker. These capabilities seamlessly integrate model development into an organization’s existing DevSecOps workflow, contributing to enhanced transparency regarding each version of the model. According to JFrog, this improvement significantly improves the visibility and understanding of the model versions throughout the development process.