JFrog introduced a new integration between JFrog Artifactory and Amazon SageMaker to streamline the process of building, training, and deploying machine learning (ML) models. This integration will allow companies to manage their ML models with the same efficiency and security as other software components in a DevSecOps workflow. 

In the new integration, ML models are immutable, traceable, secure, and validated. Additionally, JFrog has enhanced its ML Model management solution with new versioning capabilities, ensuring that compliance and security are integral parts 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,” said Kelly Hartman, SVP of global channels and alliances at JFrog. “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.”

A Forrester survey found that half of the data decision-makers see the application of governance policies within AI/ML as a major challenge for its widespread use, and 45% view data and model security as a key issue. 

JFrog’s integration with Amazon SageMaker addresses these concerns by applying DevSecOps best practices to ML model management. This allows developers and data scientists to enhance and speed up the development of ML projects while ensuring enterprise-grade security and compliance with regulatory and organizational standards, JFrog explained.

JFrog has also introduced new versioning capabilities in its ML Model Management solution, complementing its Amazon SageMaker integration. These capabilities integrate model development more seamlessly into an organization’s existing DevSecOps workflow. According to JFrog, this enhancement significantly increases transparency regarding each version of the model.