quantum leap

Ensuring a trusted software supply chain is a formidable challenge for modern enterprises. It demands robust automation to ensure compliance with organizational standards, evolving market demands, and new regulatory requirements—while effectively managing risk. Scaling this effort across hundreds or thousands of teams introduces additional complexities, including governing compliance, embedding process proofs in deliverables, and mitigating risks across diverse applications. The rise of ML-driven applications further amplifies these challenges.

This session will examine key market transitions driving the imperative for a fully governed and secure software supply chain. We will explore how enterprises can leverage the JFrog Platform’s next-gen capabilities to implement an EveryOps approach at scale, enabling seamless governance, security, and operational excellence across the organization.

Managing trust across application releases requires more than securing individual packages. JFrog’s “Trusted Applications” introduces a new way to achieve software supply chain compliance by combining evidence-based policies with end-to-end lifecycle management.

With JFrog’s platform, organizations can define applications, map resources like code and binaries, and enforce compliance throughout the release process.

By collecting and evaluating evidence at each lifecycle stage, teams ensure only secure, trusted application releases are promoted to production. Join us to see how this approach transforms application management and simplifies compliance with a single source of truth.

As the world of DevOps, DevSecOps and MLOps is going through consolidation and AI is changing the way we develop and release software, there are a lot of new opportunities in our domain. In this Panel, Amol Shukla, Thomas Domke and Shlomi Ben Haim will discuss the future of EveryOps and the opportunities and risks the future holds.

Customer feedback is more than just a suggestion box for us—it’s the driving force behind our evolution at JFrog! In this fast-paced session, we’ll share with you how your insights have directly influenced our product advancements across the Platform, from enhanced Curation & Contextual Scanning capabilities to fortified Runtime Security measures and improved Package Archival solutions. Together, we’ll showcase the power of collaboration and see how your input tangibly fuels the future of JFrog.

As a leading global automaker, Ford Motor Company is transforming its software development and manufacturing ecosystem from on-premise solutions to a hybrid cloud infrastructure. This session explores how Ford leverages JFrog’s platform to streamline software delivery and enhance DevSecOps, including the “one platform experience” with GitHub. Beyond modernization, Ford focuses on security, performance optimization, and compliance in an industry driven by software. Discover the challenges, strategies, and outcomes as Ford navigates regulations, scales operations, and ensures reliability—actively shaping the future of mobility.

As part of a trusted digital pipeline, AI and machine learning models are driving a quantum leap forward in productivity. However, releasing AI and ML models along with their dependencies has become a critical pinch point between delivering intelligent software and falling behind in your company’s innovation. As development teams attempt to bridge gaps between their DevSecOps processes and their data science counterparts, you need a way to apply your established and trusted DevOps and DevSecOps processes to your AI initiatives.

Join us as we explore “Frog ML,” a smart and opinionated Python library aimed to provide advanced ML model management capabilities by seamlessly integrating JFrog Artifactory as the primary model store and leveraging the newly introduced JFrog ML solution.

We’ll explore the concepts of unified model management for all your organization’s models, advanced deployment capabilities that enable building, deploying, and monitoring models, and experiment tracking to ensure lineage and provenance. Additionally, we’ll highlight how Frog ML helps enforce compliance, detect vulnerabilities in models, and ensure end-to-end security across the ML pipeline.