We prototype and iterate upon our models using SageMaker Notebooks. Eager mode lets us prototype models quickly by building a new computational graph for each training batch; the sequence of operations can change from iteration to iteration to accommodate different data structures or to jibe with intermediate results. That frees us to adjust the network during training without starting over from scratch. These dynamic graphs are particularly valuable for recursive computations based on variable sequence lengths, such as the words, sentences, and paragraphs in an ad that are analyzed with NLP.