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Managing ML models in bioinformatics

There’s a lot of buzz around machine learning in the life sciences, but let’s be honest—the reality doesn’t quite match the hype. 

Over 80% of computational biologists and bioinformaticians admitted they aren’t using ML at all. Surprising? Maybe not.

This gap between expectation and reality is a big issue. But we believe it’s one that can be fixed.

Join us for a webinar introducing Code Ocean 3.0: Models. This new suite of features lets your team develop, train, track and register machine learning models in a unified, traceable environment with complete model provenance.

We have discussed:
  • Why there’s such a huge gap between hype and adoption in ML
  • Your options to reduce this gap and increase adoption
  • The “missing link” in most ML workflows
  • An overview of our new ML features
  • A live demo from our VP of Product, Daniel Koster
  • Audience Q&A

Speakers

Daniel Koster

VP of Product, Code Ocean

Daniel Koster

VP of Product, Code Ocean

Daniel Koster

Daniel oversees Code Ocean’s product strategy, internal interfaces, and client-facing activities. Previous achievements include the development of a deep learning platform for plant trait phenotyping and patented software for early detection of disease symptoms in plants. Daniel’s academic contributions include publications in single-molecule biophysics of topoisomerases, bacterial predator-prey dynamics on microfabricated habitats, and deep learning-based fruit detection in commercially relevant scenarios. He has two articles published on the cover of the esteemed journal Nature and over 1,200 citations. Daniel holds a PhD from Delft University of Technology and was a postdoctoral fellow at the Weizmann Institute of Science. Daniel is passionate about scientific innovation and playback theater.