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 is seen as a fundamental shift in which organizations can approach making machine learning more accessible to both experts and non-experts.
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 significant amount of time on model selection and tuning of hyper-parameters. Automated machine learning changes that, making it easier to build and use machine learning models in the real world.
This talk is a gentle introduction into how AutoML works, and the state-of-art AutoML capabilities that are available. In this talk, you’ll learn how to use auto ML to automate selection of machine learning models and automate tuning of hyper-parameters.
During this talk, Francesca and Wee Hyong will focus on the following topics:
• Introduction to AutoML
a. Understand how AutoML works
b. Learn about the libraries and cloud services that support AutoML
• Use Case – Energy Demand Forecasting
c. Introduction to the use case
d. Data set exploration
e. Feature engineering
f. Data pre-processing
• Getting started with Automated Machine Learning
a. How to generate a forecast machine learning model using Automated machine learning
b. Perform data pre-processing with Automated machine learning
c. Perform algorithm selection with Automated machine learning
d. Perform hyperparameter selection with Automated machine learning
e. Train multiple models on a remote cluster
f. Review training results and register the best model
g. Deploy your model as web service
Francesca Lazzeri is an AI and machine learning scientist on the cloud developer advocacy team at Microsoft. Francesca has multiple years of experience as data scientist and data-driven business strategy expert; she is passionate about innovations in big data technologies and the applications of machine learning-based solutions to real-world problems. Her work on these issues covers 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 is a mentor for PhD and postdoc students at the Massachusetts Institute of Technology and 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.
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.
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