JFrog, a software supply chain provider, has partnered with Amazon SageMaker, the cloud-based machine learning platform, to enhance the development of machine learning applications. This integration allows data scientists to incorporate machine learning models into the software development lifecycle by pulling artifacts from and saving them in JFrog Artifactory. The collaboration aims to make machine learning models immutable, traceable, secure, and validated as they progress towards release.
One of the key features introduced through this partnership is the versioning capabilities for JFrog’s ML Model Management platform. These capabilities ensure that compliance and security are integrated into every stage of model development. By applying DevSecOps practices, the integration enables developers and data scientists to expand and secure machine learning projects in an enterprise-grade manner.
JFrog’s integration with Amazon SageMaker brings machine learning closer to software development and production lifecycle workflows, safeguarding models from modification or deletion. It facilitates the development, training, and securing of machine learning models. Additionally, the integration detects and blocks the use of malicious models, scans model licenses for compliance with regulations and policies, and prevents the utilization of harmful models across an organization. Furthermore, it simplifies the distribution of machine learning models as part of software releases.
To enhance the model development process, JFrog has introduced new capabilities to its ML Model Management platform. These capabilities focus on bringing model development into an organization’s secure Software Development Life Cycle (SDLC). The inclusion of versioning capabilities increases transparency around model versions, ensuring adherence to regulatory and organizational compliance.
JFrog’s collaboration with Amazon SageMaker not only addresses the challenges of machine learning model management but also emphasizes the importance of incorporating security measures throughout the development cycle. By working together, JFrog and Amazon Web Services (AWS) have created an efficient workflow that promotes speed, security, and compliance in machine learning model development in the cloud.
As organizations continue to embrace machine learning, it is essential to adopt tools and practices that facilitate seamless and secure development. JFrog’s integration with Amazon SageMaker provides a valuable solution for managing machine learning models effectively, ensuring their integrity, and enabling their secure deployment alongside software releases. With the ability to control and track models throughout their lifecycle, organizations can now leverage the power of machine learning while maintaining compliance and mitigating security risks.
