Nicolo Fusi's guest appearances

Nicolo Fusi

Microsoft Research Podcast

Chris White

Max Welling

Peter Lee

Peyton Jones

Kevin Scott

Amos Miller

Brad Smith

Nathalie Riche

Ben Zorn

Layla El Asri

Desmond Patton

Mark Hamilton

Jennifer Chayes

Andy Gordon

David Rothschild

OwlTail

This episode first aired in September, 2018: You may have heard the phrase, necessity is the mother of invention, but for Dr. Nicolo Fusi, a researcher at the Microsoft Research lab in Cambridge, Massachusetts, the mother of his invention wasn’t so much necessity as it was boredom: the special machine learning boredom of manually fine-tuning models and hyper-parameters that can eat up tons of human and computational resources, but bring no guarantee of a good result. His solution? Automate machine learning with a meta-model that figures out what other models are doing, and then predicts how they’ll work on a given dataset. On today’s podcast, Dr. Fusi gives us an inside look at Automated Machine Learning – Microsoft’s version of the industry’s AutoML technology – and shares the story of how an idea he had while working on a gene editing problem with CRISPR/Cas9 turned into a bit of a machine learning side quest and, ultimately, a surprisingly useful instantiation of Automated Machine Learning – now a feature of Azure Machine Learning – that reduces dependence on intuition and takes some of the tedium out of data science at the same time.

This episode first aired in September, 2018: You may have heard the phrase, necessity is the mother of invention, but for Dr. Nicolo Fusi, a researcher at the Microsoft Research lab in Cambridge, Massachusetts, the mother of his invention wasn’t so much necessity as it was boredom: the special machine learning boredom of manually fine-tuning models and hyper-parameters that can eat up tons of human and computational resources, but bring no guarantee of a good result. His solution? Automate machine learning with a meta-model that figures out what other models are doing, and then predicts how they’ll work on a given dataset. On today’s podcast, Dr. Fusi gives us an inside look at Automated Machine Learning – Microsoft’s version of the industry’s AutoML technology – and shares the story of how an idea he had while working on a gene editing problem with CRISPR/Cas9 turned into a bit of a machine learning side quest and, ultimately, a surprisingly useful instantiation of Automated Machine Learning – now a feature of Azure Machine Learning – that reduces dependence on intuition and takes some of the tedium out of data science at the same time.

069 - All about automated machine learning with Dr. Nicolo Fusi

You may have heard the phrase, necessity is the mother of invention, but for Dr. Nicolo Fusi, a researcher at the Microsoft Research lab in Cambridge, MA, the mother of his invention wasn’t so much necessity as it was boredom: the special machine learning boredom of manually fine-tuning models and hyper-parameters that can eat up tons of human and computational resources, but bring no guarantee of a good result. His solution? Automate machine learning with a meta-model that figures out what other models are doing, and then predicts how they’ll work on a given dataset. On today’s podcast, Dr. Fusi gives us an inside look at Automated Machine Learning – Microsoft’s version of the industry’s AutoML technology – and shares the story of how an idea he had while working on a gene editing problem with CRISPR/Cas9 turned into a bit of a machine learning side quest and, ultimately, a surprisingly useful instantiation of Automated Machine Learning - now a feature of Azure Machine Learning - that reduces dependence on intuition and takes some of the tedium out of data science at the same time.

You may have heard the phrase, necessity is the mother of invention, but for Dr. Nicolo Fusi, a researcher at the Microsoft Research lab in Cambridge, MA, the mother of his invention wasn’t so much necessity as it was boredom: the special machine learning boredom of manually fine-tuning models and hyper-parameters that can eat up tons of human and computational resources, but bring no guarantee of a good result. His solution? Automate machine learning with a meta-model that figures out what other models are doing, and then predicts how they’ll work on a given dataset. On today’s podcast, Dr. Fusi gives us an inside look at Automated Machine Learning – Microsoft’s version of the industry’s AutoML technology – and shares the story of how an idea he had while working on a gene editing problem with CRISPR/Cas9 turned into a bit of a machine learning side quest and, ultimately, a surprisingly useful instantiation of Automated Machine Learning - now a feature of Azure Machine Learning - that reduces dependence on intuition and takes some of the tedium out of data science at the same time.

043 - All About Automated Machine Learning with Dr. Nicolo Fusi