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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 permit firms to handle their ML fashions with the identical effectivity and safety as different software program elements 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, making certain that compliance and safety are integral elements of the ML mannequin growth course of.
“As extra firms start managing large information within the cloud, DevOps staff leaders are asking how they’ll scale information science and ML capabilities to speed up software program supply with out introducing threat and complexity,” mentioned Kelly Hartman, SVP of worldwide channels and alliances at JFrog. “The mix of Artifactory and Amazon SageMaker creates a single supply of reality that indoctrinates DevSecOps greatest practices to ML mannequin growth 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 information 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 greatest practices to ML mannequin administration. This enables builders and information scientists to boost and pace up the event of ML initiatives whereas making certain 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 growth extra seamlessly into a corporation’s present DevSecOps workflow. Based on JFrog, this enhancement considerably will increase transparency relating to every model of the mannequin.
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