How Automated and Interpretable Machine Learning Can Be?

Automated and Interpretable Machine Learning

Presented By Francesca Lazzeri
Francesca Lazzeri
Francesca Lazzeri
Senior Machine Learning Scientist at Microsoft

Francesca Lazzeri, Ph.D. is Senior Machine Learning Scientist at Microsoft on the Cloud Advocacy team and expert in big data technology innovations and the applications of machine learning-based solutions to real-world problems. Her research has spanned the areas of machine learning, statistical modeling, time series econometrics and forecasting, and a range of industries – energy, oil and gas, retail, aerospace, healthcare, and professional services.

Francesca periodically teaches applied analytics and machine learning classes at universities and research institutions around the world. She is Data Science mentor for Ph.D. and Postdoc students at the Massachusetts Institute of Technology, and speaker at academic and industry conferences - where she shares her knowledge and passion for AI, machine learning, and coding.

Presentation Description

Automated machine learning is based on a breakthrough from Microsoft’s Research Division. The approach combines ideas from collaborative filtering and Bayesian optimization to search an enormous space of possible machine learning pipelines intelligently and efficiently, serving essentially as a recommender system for machine learning pipelines. On the other hand, when using an algorithm’s outcomes to make high-stakes decisions, it’s important to know which features it did and did not take into account. Additionally, if a model isn’t highly interpretable, the business might not be legally permitted to use its insights to make changes to processes.


In this talk, Francesca Lazzeri will explain how you can combine automated machine learning and model interpretability to accelerate AI while still making sure that models are highly interpretable, minimizing model risk and making it easy for any enterprise to comply with regulations and best practices.

Presentation Curriculum

Automated and Interpretable Machine Learning
78:01
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