In this episode of the Data Exchange I speak with Marco Ribeiro,  Senior Researcher at Microsoft Research, and lead author of the award-winning paper ”Beyond Accuracy: Behavioral Testing of NLP models with CheckList”.  As machine learning gains importance across many application domains and industries, there is a growing need to formalize how ML models get built, deployed, and used.  MLOps is an emerging set of practices focused on productionizing the machine learning lifecycle, that draws ideas from CI/CD. But even before we talk about deploying a model to production, how do we inject more rigor into the model development process?Subscribe: Apple, Android, Spotify, Stitcher, Google, and RSS.Download the 2020 NLP Survey Report and learn how companies are using and implementing natural language technologies.Detailed show notes can be found on The Data Exchange web site.Subscribe to The Gradient Flow Newsletter.

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