AI/ML techniques play a key role in 5G/6G networks providing connectivity to IoT devices. In such scenarios, not only is it necessary to run time-sensitive applications with strict latency requirements without human intervention, but it is also key to apply automation techniques at both the application and the network levels. The chapter is composed of three sections. In the first section, we present different cloud native (CN) technologies enabling scalable, cost-efficient, and reliable IoT solutions. The second section details different distributed and hierarchical monitoring frameworks and metrics collection schemes as inputs to AI engines. In the last section, application placement problems focused on delay minimization in geographically distributed single- cluster environments are first discussed. Afterwards, application placement issues ensuring latency requirements for the applications and energy consumption in distributed multi-access edge computing (MEC) systems using AI pipelines are presented.