In science and engineering fields, obtaining accurate predictions for products and processes can be the difference between success and failure. This becomes even more critical when considering the inherent complexity and interaction effects of inputs, as one sees in creating biotechnology manufacturing processes. Traditional modeling methods fail to accurately predict for complex systems, leaving experts unable to fully explain observed behavior.

Now, it’s easier than ever to build accurate, reliable predictive models that can handle such complexity, using modern machine learning methods built precisely for small experimental data.

In this webcast conversation, Predictum’s Philip Ramsey will explain how this is possible using the  Self-Validating Ensemble Modeling (SVEM) methodology, an easy-to-use, new modeling technique that results in a greater ability to accurately predict. Furthermore, Ramsey will share recent enhancements to SVEM, further extending its power and ease of use.

Advances in Small Experimental Designs and Machine Learning