  Dynamic network slicing has emerged as a promising and fundamental framework
for meeting 5G's diverse use cases. As machine learning (ML) is expected to
play a pivotal role in the efficient control and management of these networks,
in this work we examine the ML-based Quality-of-Transmission (QoT) estimation
problem under the dynamic network slicing context, where each slice has to meet
a different QoT requirement. We examine ML-based QoT frameworks with the aim of
finding QoT model/s that are fine-tuned according to the diverse QoT
requirements. Centralized and distributed frameworks are examined and compared
according to their accuracy and training time. We show that the distributed QoT
models outperform the centralized QoT model, especially as the number of
diverse QoT requirements increases.
