This is a continued presentation around Artificial Intelligence (AI) from the BalticNOG 2025 where I covered the fundamental difference between AI for Networks and AI Networks - the intent of this talk is to cover more details around the challenges posed by AI workloads in data center infrastructure for TRAINING and INFERENCE, as these workloads pose some challenges around the data center infrastructure. This talk will cover fundamentals around how data is collected and structured/cleaned up for training usage as well as once the training is done how the model will be used for inference. It will look at data center design including AI Fabrics, scale-up vs. scale-out designs, challenges, and protocols used and provide some future guidelines around enhancements to such challenges under works within the UEC (Ultra Ethernet Consortium). UEC goal is to deliver a complete architecture that optimizes Ethernet for high performance AI and HPC networking, exceeding the performance of today’s specialized technologies.  UEC specifically focuses on functionality, performance, TCO, and developer and end-user friendliness, while minimizing changes to only those required and maintaining Ethernet interoperability