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ML coding aims for larger leap with JFrog-Amazon integration

Developer software company JFrog is offering an integration with Amazon SageMaker that it says will help customers build, train and deploy machine learning (ML) models.

Kelly Hartman, senior vice president of global channels and alliances at JFrog, said the combination of Artifactory and Amazon SageMaker created “a single source of truth” that supports devsecops best practice in the cloud.

“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,” Hartman said.

The idea is that ML model development using the integration will offer more flexibility, speed, and security, according to the announcement.

Pairing JFrog Artifactory with Amazon SageMaker would deliver “immutable, traceable, secure and validated” ML models alongside other software development components in the devsecops workflow as each release is developed.

“JFrog also unveiled new versioning capabilities for its ML Model management solution, which help ensure compliance and security are incorporated at every step of ML model development,” the vendor added, proffering Forrester figures suggesting that governance policies can be a major challenge to AI//ML adoption.

The new integration would allow developers and data scientists to expand, accelerate, and secure their ML projects, the vendor said, ensuring all models are readily accessible, traceable, and tamper-proof, as well as bringing ML closer to the software development and production lifecycle workflows, which it said would help protect models from unauthorised changes.

It could also help devsecops detect and block malicious ML modelling and ensure licence compliance, with versioning and access also more transparent and all artifacts pulled from and saved in JFrog Artifactory, it added.

( Image copyright © JFrog 2024 )

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