end to end learning

End to end learning is a machine learning approach where a single model learns to map raw inputs directly to outputs, bypassing traditional hand-crafted pipelines. On WindowsForum, discussions center on its application in autonomous driving, particularly Wayve's use of deep learning and Microsoft Azure to scale city driving. The tag covers how end to end learning contrasts with modular systems, relying on data-driven training rather than explicit rules or HD maps. Recurring themes include the practical challenges of deploying such models in real-world environments, the role of cloud infrastructure for training and inference, and the trade-offs between end-to-end and hybrid architectures. The content reflects interest in AI, deep learning, and enterprise cloud integration within the Windows ecosystem.