JFrog Ltd., the creators of the JFrog Software Supply Chain Platform, announced an integration with Amazon SageMaker. This collaboration allows companies to develop, train, and deploy machine learning models seamlessly using fully managed infrastructure, tools, and workflows. Combining JFrog Artifactory with Amazon SageMaker enables the delivery of ML models within a modern DevSecOps workflow. This ensures that each model is immutable, traceable, secure, and validated throughout its development for release. Additionally, JFrog introduced new versioning capabilities for its ML Model management solution to enhance compliance and security at every stage of ML model 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, Global Channels and Alliances, JFrog. “The combination of JFrog 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.”
According to a recent Forrester survey, 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’s 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.
JFrog’s new Amazon SageMaker integration allows organizations to:
- Maintain a single source of truth for data scientists and developers, ensuring all models are readily accessible, traceable, and tamper-proof.
- Bring ML closer to the software development and production lifecycle workflows, protecting models from deletion or modification.
- Develop, train, secure and deploy ML models.
- Detect and block the use of malicious ML models across the organization.
- Scan ML model licenses to ensure compliance with company policies and regulatory requirements.
- Store home-grown or internally augmented ML models with robust access controls and versioning history for greater transparency.
- Bundle and distribute ML models as part of any software release.
“Traditional software development processes and machine learning stand apart, lacking integration with existing tools,” said Larry Carvalho, Principal and founder of RobustCloud. “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 harmonizing machine learning pipelines with established software development lifecycles and best practices.”
Along with its Amazon SageMaker integration, JFrog unveiled new versioning capabilities for its ML Model Management solution that incorporate model development into an organization’s DevSecOps workflow to increase transparency around each model version so developers, DevOps teams, and data scientists can ensure the correct, secure version of a model is utilized.
The JFrog integration with Amazon SageMaker, available now for JFrog customers and Amazon SageMaker users, ensures all artifacts consumed by data scientists or used to develop ML applications are pulled from and saved in JFrog Artifactory.
For a deeper look at the JFrog Artifactory integration and how it works, read this blog. You can also register to join JFrog and AWS on Wednesday, January 31 for an educational webinar, “Building for the future: DevSecOps in the era of AI/ML model development,” describing best practices for introducing model use and development into secure software supply chain and development processes.