  Future machine learning (ML) powered applications, such as autonomous driving
and augmented reality, involve training and inference tasks with timeliness
requirements and are communication and computation intensive, which demands for
the edge learning framework. The real-time requirements drive us to go beyond
accuracy for ML. In this article, we introduce the concept of timely edge
learning, aiming to achieve accurate training and inference while minimizing
the communication and computation delay. We discuss key challenges and propose
corresponding solutions from data, model and resource management perspectives
to meet the timeliness requirements. Particularly, for edge training, we argue
that the total training delay rather than rounds should be considered, and
propose data or model compression, and joint device scheduling and resource
management schemes for both centralized training and federated learning
systems. For edge inference, we explore the dependency between accuracy and
delay for communication and computation, and propose dynamic data compression
and flexible pruning schemes. Two case studies show that the timeliness
performances, including the training accuracy under a given delay budget and
the completion ratio of inference tasks within deadline, are highly improved
with the proposed solutions.
