Better, cheaper and more abundant batteries will be a key driver of a faster transition to sustainability.  Improving batteries requires pushing the boundaries and empirically comparing many variations on design.  Therefore, quantifying and comparing important issues and performance metrics enables faster improvements. In this session, we use neural nets and various data-oriented processes to extract such a metric  from the raw timeseries cycling data from scratch,  and present our learnings integrating various ideas such as structured labels, ensembles, distillation, various active learning techniques as well as model-assisted quality control of data to tame this challenging data modality.