JFrog & AWS: Accelerating Secure ML Development
JFrog has announced a new integration with Amazon SageMaker, 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 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, which help ensure compliance and security are incorporated at every step of ML model development.
Kelly Hartman, SVP, Global Channels and Alliances, JFrog, said, “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. 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.”
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.
Larry Carvalho, Principal and founder of RobustCloud, said, “Traditional software development processes and machine learning stand apart, lacking integration with existing tools. 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.
