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 corporations 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 answer with new versioning capabilities, guaranteeing that compliance and safety are integral components of the ML mannequin growth course of.
“As extra corporations start managing large knowledge within the cloud, DevOps crew leaders are asking how they’ll scale knowledge science and ML capabilities to speed up software program supply with out introducing danger 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 fact that indoctrinates DevSecOps greatest practices to ML mannequin growth within the cloud – delivering flexibility, velocity, 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 applying of governance insurance policies inside AI/ML as a significant problem for its widespread use, and 45% view knowledge and mannequin safety as a key challenge.
JFrog’s integration with Amazon SageMaker addresses these issues by making use of DevSecOps greatest practices to ML mannequin administration. This enables builders and knowledge scientists to boost and velocity up the event of ML tasks 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 answer, complementing its Amazon SageMaker integration. These capabilities combine mannequin growth extra seamlessly into a company’s current DevSecOps workflow. In accordance with JFrog, this enhancement considerably will increase transparency concerning every model of the mannequin.
