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?

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