JFrog, a well-known provider of software supply chain solutions, has responded to the growing need for a central management system to align AI deliveries with an organization’s existing DevOps practices. As businesses increasingly rely on various AI implementations within their services, JFrog’s new capabilities, known as “ML model management,” aim to address the challenge of managing an organization’s local and open source ML models while ensuring their security throughout the software development lifecycle (SDLC).
The introduction of ML model management within the JFrog software supply chain platform is a significant step towards helping customers deliver trusted software at scale. JFrog, known for creating Artifactory, the industry’s leading technology for storing, managing, and securing binaries, brings the same expertise to manage another type of binary: ML models. Yoav Landman, the chief technology officer and co-founder of JFrog, expressed pride in expanding the platform’s capabilities to include ML models, demonstrating JFrog’s commitment to meeting the evolving needs of their customers.
In addition to ML model management, JFrog has also announced the integration of Release Lifecycle Management (RLM) and a suite of new security capabilities in their platform. RLM introduces a valuable functionality that allows organizations to create an immutable “Release bundle” early in the software development lifecycle. This bundle defines a potential release and its components, providing a comprehensive overview of the release process. With the help of anti-tampering systems, compliance checks, and evidence capture, RLM collects data and insights at each stage of the SDLC, ensuring transparency and traceability.
The introduction of these new DevOps functionalities further enhances the JFrog platform and strengthens its position as a leading provider of software delivery solutions. By integrating ML model management and RLM, JFrog offers organizations a comprehensive solution to effectively manage their software development processes while ensuring the security and integrity of ML models.
The benefits of adopting JFrog’s ML model management and RLM are manifold. With ML model management, organizations gain centralized control over their ML models, allowing for easy storage, management, and security. By incorporating ML models into the existing software supply chain, organizations can streamline their development processes, promote collaboration, and reduce the risks associated with managing ML models individually.
Similarly, RLM plays a crucial role in improving the overall release management process. By creating an immutable release bundle, organizations can establish a clear and standardized approach to releasing software. The incorporation of anti-tampering systems and compliance checks ensures the integrity of each release, safeguarding against unauthorized modifications. Additionally, the evidence capture feature provides valuable insights into the release process, allowing for deeper analysis and continuous improvements.
Overall, JFrog’s latest additions to their platform are designed to empower organizations to deliver trusted software at scale. By enabling ML model management and introducing RLM, JFrog provides a comprehensive solution that aligns AI deliveries with DevOps practices. These capabilities not only streamline software development processes but also enhance security, compliance, and collaboration. As businesses continue to rely on AI implementations, JFrog’s focus on meeting the evolving needs of their customers positions them as a leader in the software supply chain industry.