JFrog-Amazon SageMaker integration aims to streamline machine learning workflows


Software supply chain company JFrog Ltd. announced today a new integration with Amazon SageMaker to enable developers and data scientists to collaborate efficiently on building, training and deploying machine learning models.

SageMaker is a cloud-based machine-learning platform that allows the creation, training and deployment of machine learning models on the cloud and is used by developers to deploy them on embedded systems and edge devices. The new pairing with JFrog’s Artifactory allows the models to be incorporated into a modern software development life cycle, making each model immutable, traceable, secure and validated as it matures for release.

The integration has been designed to address concerns around artificial intelligence and machine learning. A recent Forrester Consulting survey found that 50% of data decision-makers cited applying governance policies within AI and machine learning as the biggest challenge to widespread usage, while 45% cited data and model security as the gating factor.

JFrog’s SageMaker integration has been designed to address these concerns by integrating DevSecOps best practices into ML model management, allowing developers and data scientists to expand, secure and grow their ML projects while ensuring security, regulatory and organizational compliance.

“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, senior vice president of global channels and alliances at JFrog. “Working with AWS, we’ve been able to design a workflow that indoctrinates DevSecOps best practices to ML model development in the cloud, delivering flexibility, speed, security and peace of mind.”

Key features of the integration bring machine learning closer to the standard software development and production lifecycles, ensuring enhanced protection against deletion or modification of models. The integration enables the development, training, securing and deployment of models in a more streamlined manner.

The integration also provides capabilities to detect and block the use of malicious models within the organization, enhancing security and tools for scanning model licenses and ensuring they comply with company policies and regulatory requirements.

For improved transparency and control, the integration supports storing homegrown and internally augmented models with robust access controls and detailed versioning history. The integration simplifies the process of bundling and distributing models as part of regular software releases, aligning machine learning development more closely with traditional software deployment processes.

Along with its SageMaker integration, JFrog also unveiled new versioning capabilities for its ML Model Management solution today that assist in bringing model development into an organization’s secure and compliant SDLC. The new versioning capabilities increase transparency around each model version, allowing developers, DevOps teams and data scientists to ensure that the right version is being used at the right place and time while also ensuring that it’s secure.

Image: JFrog

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