Enterprise-Scale Artifact Management for Modern AI/ML

As enterprise AI adoption accelerates, artifact management evolves from simple asset storage to a strategic enabler powering modern AI/ML pipelines. Organizations now face massive binaries, unpredictable CI/CD workloads, rapid model iteration, and GPU-accelerated development all requiring a scalable, resilient artifact management strategy. This session shares proven DevOps patterns and lessons from real-world transformations, offering a blueprint for building cloud-native, hyper-scale artifact platforms that meet the demands of industrial-scale AI. Key Industry Takeaways and Practices: - Scaling for Hyper-Growth - Metrics-Driven SRE: Observability for Reliability - Best Practices for AI Artifact Management - Operationalizing AI Model Supply Chains at Scale The session will also highlight how advanced artifact platforms such as managing NVIDIA AI modules within JFrog streamline enterprise GPU workloads and accelerate trusted AI outcomes.