Case Study: Biopharma

PREDICTING YIELD AND DETECTING ANOMALIES WITH MACHINE LEARNING

Our customer, a global pharmaceutical manufacturer, is looking to reduce costs and increase efficiency.
The company has been systematically collecting data for the past 10 years and is now interested in turning the massive datasets into actionable insights.  

Specifically, our customer wanted to understand:

  • How to optimize the bioreactor process to reach higher yield
  • How to identify prespecified anomalies in the 50 day process
  • How can they make monitoring of large amount of process parameters easier