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Moving generative AI applications from the proof of concept stage into production requires control, reliability and data governance. Organizations are turning to open source foundation models in search of that control and the ability to better influence outputs by more tightly managing both the models and the data they are trained on.Databricks has assisted thousands of customers in evaluating use cases for generative AI and determining the most appropriate architecture for their organization.Our customers have shared with us the challenge of building and deploying production-quality AI models, which is often difficult and expensive. As a result, most CIOs are not…

arXiv:2405.14932v1 Announce Type: new Abstract: Multi-dimensional parameter spaces are commonly encountered in astroparticle physics theories that attempt to capture novel phenomena. However, they often possess complicated posterior geometries that are expensive to traverse using techniques traditional to this community. Effectively sampling these spaces is crucial to bridge the gap between experiment and theory. Several recent innovations, which are only beginning to make their way into this field, have made navigating such complex posteriors possible. These include GPU acceleration, automatic differentiation, and neural-network-guided reparameterization. We apply these advancements to astroparticle physics experimental results in the context of novel neutrino physics and…