  Deep learning ship detection in satellite optical imagery suffers from false
positive occurrences with clouds, landmasses, and man-made objects that
interfere with correct classification of ships, typically limiting class
accuracy scores to 88\%. This work explores the tensions between customization
strategies, class accuracy rates, training times, and costs in cloud based
solutions. We demonstrate how a custom U-Net can achieve 92\% class accuracy
over a validation dataset and 68\% over a target dataset with 90\% confidence.
We also compare a single node architecture with a parameter server variant
whose workers act as a boosting mechanism. The parameter server variant
outperforms class accuracy on the target dataset reaching 73\% class accuracy
compared to the best single node approach. A comparative investigation on the
systematic performance of the single node and parameter server variant
architectures is discussed with support from empirical findings.
