Put AI to Work
April 15-18, 2019
New York, NY
Wee Hyong Tok

Wee Hyong Tok
Principal Data Science Manager, Microsoft

Website | @weehyong

Wee Hyong Tok is a principal data science manager with the AI CTO office at Microsoft, where he leads the engineering and data science team for the AI for Earth program. Wee Hyong has worn many hats in his career, including developer, program and product manager, data scientist, researcher, and strategist, and his track record of leading successful engineering and data science teams has given him unique superpowers to be a trusted AI advisor to customers. Wee Hyong coauthored several books on artificial intelligence, including Predictive Analytics Using Azure Machine Learning and Doing Data Science with SQL Server. Wee Hyong holds a PhD in computer science from the National University of Singapore.

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: ****.
(4.17, 6 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.