JFrog and AWS partner to integrate machine learning with cloud development
Press Release by JFrog Wed 17 Jan 2024
JFrog, a software supply chain provider, has announced its integration with Amazon SageMaker, an AWS cloud-based machine learning platform.
This integration aims to embed machine learning models into the software development lifecycle.
The integration, which is now available, ensures that artifacts created during the development of machine learning applications are securely managed within the JFrog Artifactory.
Announced on January 17, this collaboration between JFrog and Amazon ensures that machine learning models are immutable, traceable, secure, and validated throughout their development.
“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,” said Kelly Hartman, SVP of Global Channels and Alliances at JFrog.
Enhancing Machine Learning with DevSecOps Practices
This partnership between JFrog and AWS applies DevSecOps practices to machine learning model management. It allows developers and data scientists to enhance and secure the development of machine learning projects within an enterprise-grade framework.
The integration is designed to bring machine learning closer to software development and production lifecycle workflows, thereby protecting models from unauthorised modification or deletion.
“The combination of Artifactory and Amazon SageMaker creates a single source of truth that indoctrinates DevSecOps best practices to ML model development in the cloud – delivering flexibility, speed, security, and peace of mind – breaking into a new frontier of MLSecOps,” added Hartman.
According to a recent Forrester survey, 50% of data decision-makers cited applying governance policies within AI/ML as the biggest challenge to widespread usage, while 45% cited data and model security as the gating factor.
“Traditional software development processes and machine learning stand apart, lacking integration with existing tools,” said Larry Carvalho, Principal and Founder of RobustCloud.
Users of the JFrog-Amazon SageMaker integration can expect development, training, and securing of machine learning models. They can also benefit from detection and blocking of the use of malicious models.
Users can scan model licenses to ensure compliance with regulatory requirements and company policies, as well as distribute machine learning models as part of a software release.
“Together, JFrog Artifactory and Amazon SageMaker provide an integrated end-to-end, governed environment for machine learning. Bringing these worlds together represents significant progress towards harmonising machine learning pipelines with established software development lifecycles and best practices,” added Carvalho.
JFrog has also introduced versioning capabilities for its ML Model Management platform, integrating model development into an organisation’s secure Software Development Life Cycle (SDLC). These new features increase transparency around model versions and ensure that projects adhere to regulatory and organisational compliance standards.