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

Using AutoML to automate selection of machine learning models and hyperparameters

Francesca Lazzeri (Microsoft), Wee Hyong Tok (Microsoft)
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
Average rating: *****
(5.00, 2 ratings)

Who is this presentation for?

  • Data scientists, data engineers, and AI developers

Level

Intermediate

Prerequisite knowledge

  • A working knowledge of Python
  • A basic understanding of machine learning and deep learning topics and terminology
  • Familiarity with time series forecasting (useful but not required)

What you'll learn

  • Understand the key concepts and principles of time series forecasting
  • Learn how to set up an environment for performing AutoML, build different machine learning models using AutoML libraries, review training results, register the best model, deploy, and test it

Description

Automated machine learning (AutoML) enables both data scientists and domain experts (with limited machine learning training) to be productive and efficient. In recent years, AutoML has fostered a fundamental shift in how organizations approach machine learning, making it more accessible to both experts and nonexperts.

Most real-world data science projects are time-consuming, resource intensive, and challenging. Besides data preparation, data cleaning, and feature engineering, data scientists often spend a significant amount of time on model selection and tuning of hyperparameters. Automated machine learning changes that, making it easier to build and use machine learning models in the real world.

Francesca Lazzeri and Wee Hyong Tok lead a gentle introduction to how AutoML works and the state-of-art AutoML capabilities that are available. You’ll learn how to use AutoML to automate selection of machine learning models and automate tuning of hyperparameters.

Topics include:

  • An introduction to AutoML
  • How AutoML works
  • The libraries and cloud services that support AutoML
  • An energy demand forecasting use case
  • How to get started with automated machine learning
Photo of Francesca Lazzeri

Francesca Lazzeri

Microsoft

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.

Photo of Wee Hyong Tok

Wee Hyong Tok

Microsoft

Wee Hyong Tok is a principal data science manager with Microsoft. 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.

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