JFrog and Amazon SageMaker Collaborate to Streamline Machine Learning Workflows

Software supply chain company JFrog announced a new integration with Amazon SageMaker to streamline machine learning workflows. This collaboration allows developers and data scientists to collaborate efficiently on building, training, and deploying machine learning models. JFrog’s integration with SageMaker’s cloud-based machine-learning platform enables the models to be incorporated into a modern software development life cycle, ensuring immutability, traceability, security, and validation. The integration addresses concerns surrounding AI and machine learning such as governance policies, data and model security, and compliance. It incorporates DevSecOps best practices into ML model management, allowing developers and data scientists to expand, secure, and grow their ML projects while ensuring security and compliance. The integration brings machine learning closer to standard software development and production lifecycles, enhancing protection against deletion or modification of models. It also provides capabilities to detect and block the use of malicious models, ensures compliance with company policies and regulatory requirements, and supports homegrown and internally augmented models with detailed versioning and access control. JFrog also unveiled new versioning capabilities for its ML Model Management solution, increasing transparency and ensuring the right version is used at the right place and time. This integration aligns machine learning development with traditional software deployment processes, simplifying the bundling and distribution of models as part of regular software releases. Overall, JFrog’s collaboration with Amazon SageMaker aims to streamline and secure machine learning workflows, providing flexibility, speed, security, and peace of mind for DevOps team leaders managing big data in the cloud.