{"id":3068007,"date":"2024-01-17T20:06:42","date_gmt":"2024-01-18T01:06:42","guid":{"rendered":"https:\/\/wordpress-1016567-4521551.cloudwaysapps.com\/plato-data\/jfrog-and-aws-accelerate-secure-machine-learning-development\/"},"modified":"2024-01-17T20:06:42","modified_gmt":"2024-01-18T01:06:42","slug":"jfrog-and-aws-accelerate-secure-machine-learning-development","status":"publish","type":"station","link":"https:\/\/platodata.io\/plato-data\/jfrog-and-aws-accelerate-secure-machine-learning-development\/","title":{"rendered":"JFrog and AWS Accelerate Secure Machine Learning Development"},"content":{"rendered":"

\nNew JFrog Artifactory and Amazon SageMaker integration empowers developers and data scientists to build, train, and deploy ML Models in the cloud<\/i><\/p>\n

SUNNYVALE, Calif.\u2013(BUSINESS WIRE)\u2013JFrog Ltd.<\/a> (\u201cJFrog\u201d) (Nasdaq: FROG), the Liquid Software company and creators of the JFrog Software Supply Chain Platform<\/a>, today announced a new integration with Amazon SageMaker<\/a>, which helps companies build, train, and deploy machine learning (ML) models for any use case with fully managed infrastructure, tools, and workflows. By pairing JFrog Artifactory<\/a> with Amazon SageMaker, ML models can be delivered alongside all other software development components in a modern DevSecOps workflow, making each model immutable, traceable, secure, and validated as it matures for release. JFrog also unveiled new versioning capabilities for its ML Model management solution<\/a>, which help ensure compliance and security are incorporated at every step of ML model development.<\/p>\n

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\n\u201cAs 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,\u201d said Kelly Hartman, SVP, Global Channels and Alliances, JFrog. \u201cThe combination of Artifactory and Amazon SageMaker creates a single source of truth that indoctrinates DevSecOps best practices to ML model development in the cloud \u2013 delivering flexibility, speed, security, and peace of mind \u2013 breaking into a new frontier of MLSecOps.\u201d<\/p>\n

\nAccording to a recent Forrester survey<\/a>, 50 percent of data decision-makers cited applying governance policies within AI\/ML as the biggest challenge to widespread usage, while 45 percent cited data and model security as the gating factor. JFrog\u2019s Amazon SageMaker integration applies DevSecOps best practices to ML model management, allowing developers and data scientists to expand, accelerate, and secure the development of ML projects in a manner that is enterprise-grade, secure, and abides by regulatory and organizational compliance.<\/p>\n

\nJFrog\u2019s new Amazon SageMaker integration<\/a> allows organizations to:<\/p>\n