Put AI to Work
April 15-18, 2019
New York, NY
Francesca Lazzeri

Francesca Lazzeri
Machine Learning Scientist, Microsoft

Website | @frlazzeri

Francesca Lazzeri is a machine learning scientist on the cloud advocacy team at Microsoft. An expert in big data technology innovations and the applications of machine learning-based solutions to real-world problems, she has worked with these issues in a wide range of industries, including energy, oil and gas, retail, aerospace, healthcare, and professional services. Previously, she was a research fellow in business economics at Harvard Business School, where she performed statistical and econometric analysis within the Technology and Operations Management Unit and worked on multiple patent data-driven projects to investigate and measure the impact of external knowledge networks on companies’ competitiveness and innovation. Francesca periodically teaches applied analytics and machine learning classes at universities in USA and Europe and is a mentor for PhD and postdoc students at the Massachusetts Institute of Technology. She enjoys speaking at academic and industry conferences to share her knowledge and passion for AI, machine learning, and coding. Francesca holds a PhD in innovation management.

Sessions

9:00am - 5:00pm Monday, April 15 & Tuesday, April 16
Secondary topics:  Deep Learning and Machine Learning tools, Financial Services, Models and Methods, Temporal data and time-series
Francesca Lazzeri (Microsoft), Wee Hyong Tok (Microsoft), Krishna Anumalasetty (Microsoft)
Francesca Lazzeri, Wee Hyong Tok, and Krishna Anumalasetty walk you through the core steps for using Azure Machine Learning services to train your machine learning models both locally and on remote compute resources. Read more.
1:00pm1:40pm Thursday, April 18, 2019
Machine Learning, Models and Methods
Location: Grand Ballroom West
Secondary topics:  AI case studies, Automation in machine learning and AI, Deep Learning and Machine Learning tools
Francesca Lazzeri (Microsoft), Wee Hyong Tok (Microsoft)
Average rating: *****
(5.00, 2 ratings)
Automated machine learning (AutoML) enables both data scientists and domain experts (with limited machine learning training) to be productive and efficient. AutoML is a fundamental shift in how organizations approach machine learning. Francesca Lazzeri and Wee Hyong Tok demonstrate how to use AutoML to automate the selection of machine learning models and automate tuning of hyperparameters. Read more.