  Batch processes show several sources of variability, from raw materials'
properties to initial and evolving conditions that change during the different
events in the manufacturing process. In this chapter, we will illustrate with
an industrial example how to use machine learning to reduce this apparent
excess of data while maintaining the relevant information for process
engineers. Two common use cases will be presented: 1) AutoML analysis to
quickly find correlations in batch process data, and 2) trajectory analysis to
monitor and identify anomalous batches leading to process control improvements.
