Abstract: Machine learning (ML) and AI will play a key role in the development of 6G networks. Network virtualization and network softwarization solutions in 5G networks can support data-driven intelligent and automated networks to some extent and this trend will grow in 5G-advanced networks. Radio access network algorithms and radio resource management functions can exploit network intelligence to fine tune network parameters to reach close-to-optimal performance in 5G networks. In 6G networks, network intelligence is envisioned to be end-to-end, and air interface is envisioned to be AI-native. The user equipment (UE) devices need to be smarter, environment and context aware, and capable of running ML algorithms. This talk will focus on the main practical challenges in developing machine learning solutions in 5G use cases and emphasize with a case study how deployment of these solutions is much harder in a live network as compared to theoretical performance evaluation. Further, a vision for paradigm shift from AI-as-an-enabler to AI-Native air-interface will be provided for 6G networks. Speaker(s): Dr. Majid, Virtual: https://events.vtools.ieee.org/m/329644