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.
Riffyn’s pioneering cloud system, Riffyn Nexus, delivers 2X faster development cycles and 4X productivity gains to the world’s scientific R&D organizations. Its process-centric experiment design, data capture, and data analytics overcome the limitations of traditional scientific data systems — delivering results that are always ready for machine learning.