Problem Formulation for Machine Learning with Romer Rosales

800 800 The TWIML AI Podcast (formerly This Week in Machine Learning & Artificial Intelligence)

In this episode, we’re joined by Romer Rosales, Director of AI at LinkedIn.

We begin with a discussion of graphical models and approximate probability inference, and he helps me make an important connection in the way I think about that topic. We then review some of the applications of machine learning at LinkedIn, and how what Romer calls their ‘holistic approach’ guides the evolution of ML projects at LinkedIn. This leads us into a really interesting discussion about problem formulation and selecting the right objective function for a given problem. We then talk through some of the tools they’ve built to scale their data science efforts, including large-scale constrained optimization solvers, online hyperparameter optimization and more. This was a really fun conversation, that I’m sure you’ll enjoy!

TWIML Online Meetup Update

Tomorrow, June 12th at 5 pm US Pacific Time. Kelvin Ross will be reviewing the paper Cardiologist-Level Arrhythmia Detection with Convolutional Neural Networks, which is work by researchers in Andrew Ng’s lab at Stanford. For more information visit

About Romer

Mentioned in the Interview

“More On That Later” by Lee Rosevere licensed under CC By 4.0

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