Deep learning to improve quality control in pharmaceutical manufacturing

This demo shows how deep learning can be applied in pharmaceutical industry, specifically for the reducing rejection rates of non-defect products in drug manufacturing. Advancements in convolutional neural networks for classification and variational autoencoders for anomaly detection have generated such impressive results over the past couple of years that the technology is now starting to become mature enough to be useful in the real world. Many drug manufacturers rely on highly manual, expensive processes for running their quality control operations and, until now, they haven't had a technological alternative advanced enough for being able to optimize this part of their manufacturing pipeline. Deep learning is a true game changer in this industry and being able to increase efficiency in the production of drugs leads potential huge price reductions, making modern medicine available to more people in need -- especially among low-income groups. This demo shows how these advantages can be obtained, as we will bring a professional CVT machine (capable of inspecting up to 600 cartridges or vials per minute) fitted with a chain of neural networks who run in real time to analyze the products and make decisions on whether to release or reject the products that pass by. Attendees will be able to interact with the underlying models through an easy-to-use interface that allows for retraining of models based on new datasets as well as deployment of the models. The goal of the demo is to leave attendees with the impression that neural nets are indeed ready to be deployed into highly regulated industries with the purpose of making a positive difference for all of us.