A cloud-to-edge deep learning pipeline is a fully automated conduit for training and deploying models to the edge. This enables quick model retraining and makes the solution more robust toward data shifts. Cloud-to-edge pipelines are pivotal for many applications, from autonomous vehicles to smart city infrastructure. One of the main challenges with cloud-to-edge deep learning pipelines is ensuring that there is no discrepancy between edge and cloud model performance. In this talk, we introduce cloud-to-edge deep learning pipelines. We then delve into key techniques for testing cloud-to-edge deep learning pipelines. We explore the architecture of these pipelines, emphasizing the synergy between cloud processing and edge-based inference. Key focuses include tailored testing strategies (unit, integration, system testing); balancing simulated and real-world scenarios; and evaluating performance metrics beyond accuracy, such as latency and resource utilization. Aimed at professionals, this presentation offers practical insights for developing robust, efficient ML systems.