Routinize Machine Learning to Realize Biotech Gains -- Genetic Engineering News
Riffyn argues that habits, not heroics, are needed to drive the digital transformation of bioprocessing
Bioprocessing engineers know that they can benefit from machine learning, mainly through improved control over quality and performance parameters, but they also believe that machine learning requires a lot of work—the installation, configuration, and maintenance of various data systems and tools. In fact, this work looks so daunting that most bioprocessing engineers are inclined to postpone any moves to machine learning.
Another approach is possible. It’s SDE, Riffyn’s machine learning system. According to Gardner, it has halved process development time for many of its customers, while halving the effort required from customers’ personnel. Riffyn’s SDE system flexibly integrates siloed data with visual drag-and-drop process design, automated data context and integration, user configurable file parsing, and programmatic interfaces that integrate with third-party applications.
Tim Gardner is the Founder and the CEO of Riffyn. He was previously Vice President of Research & Development at Amyris, where he led the engineering of yeast strain and processes technology for large-scale bio-manufacturing of renewable chemicals. Tim has been recognized for his pioneering work in Synthetic Biology by Scientific American, the New Scientist, Nature, Technology Review, and the New York Times. He also served as an advisor to the European Union Scientific Committees and the Boston University Engineering Alumni Advisory Board. Tim enjoys hockey, running, mountain biking, and being beaten by his sons in almost everything.