JFrog launched a brand new integration between JFrog Artifactory and Amazon SageMaker to streamline the method of constructing, coaching, and deploying machine studying (ML) fashions. This integration will enable corporations to handle their ML fashions with the identical effectivity and safety as different software program parts in a DevSecOps workflow. 

Within the new integration, ML fashions are immutable, traceable, safe, and validated. Moreover, JFrog has enhanced its ML Mannequin administration resolution with new versioning capabilities, guaranteeing that compliance and safety are integral components of the ML mannequin improvement course of.

“As extra corporations start managing massive information within the cloud, DevOps workforce leaders are asking how they’ll scale information science and ML capabilities to speed up software program supply with out introducing danger and complexity,” stated Kelly Hartman, SVP of world channels and alliances at JFrog. “The mix of Artifactory and Amazon SageMaker creates a single supply of reality that indoctrinates DevSecOps finest practices to ML mannequin improvement within the cloud – delivering flexibility, pace, safety, and peace of thoughts – breaking into a brand new frontier of MLSecOps.”

A Forrester survey discovered that half of the info decision-makers see the appliance of governance insurance policies inside AI/ML as a serious problem for its widespread use, and 45% view information and mannequin safety as a key situation. 

JFrog’s integration with Amazon SageMaker addresses these issues by making use of DevSecOps finest practices to ML mannequin administration. This enables builders and information scientists to reinforce and pace up the event of ML initiatives whereas guaranteeing enterprise-grade safety and compliance with regulatory and organizational requirements, JFrog defined.

JFrog has additionally launched new versioning capabilities in its ML Mannequin Administration resolution, complementing its Amazon SageMaker integration. These capabilities combine mannequin improvement extra seamlessly into a corporation’s current DevSecOps workflow. Based on JFrog, this enhancement considerably will increase transparency relating to every model of the mannequin.