Using automated machine learning for hyperparameter optimization and algorithm selection
Who is this presentation for?
- Data scientists, AI developers, and technical product managers
AutoML is the process of taking training data with a defined target feature and iterating through combinations of algorithms and feature selections to automatically select the best model for your data, based on the training scores. In recent years, AutoML has been seen as a fundamental shift in which organizations can approach making machine learning more accessible to both experts and nonexperts.
The traditional machine learning model development process is highly resource-intensive and requires significant domain knowledge and time investment to run and compare the results of dozens of models. AutoML simplifies this process by generating models tuned from the goals and constraints you defined for your experiment, such as the time for the experiment to run or which models to blacklist.
Francesca Lazzeri and Wee Hyong Tok detail how you can use AutoML for your own hyperparameter optimization and algorithm selection scenario. If you saw Francesca and Wee Hong at the AI Conference in New York, they’re here to give you the latest updated information. But you don’t have to have seen them before to benefit from the talk. They provide you with an introduction to AutoML and share a use case of sales forecasting, where you’ll be introduced to the use case, explore the dataset, engineer the feature, and preprocess the data. You’ll also learn the basics of AutoML, including how to generate a forecast machine learning model; perform data preprocessing, algorithm, and hyperparameter selection; explain the model; train multiple models on a remote cluster; review training results and register the best model; and deploy your model as a web service.
- Experience coding in Python
- A basic understanding of machine learning and deep learning topics and terminology
- Familiarity with Time Series Forecast (useful but not required)
What you'll learn
- Understand the key concepts and principles of time series forecast, set up your own environment for performing AutoML, build different machine learning models using AutoML libraries, review training results, register the best model, and deploy and test it
Francesca Lazzeri is an AI and machine learning scientist at Microsoft. She has multiple years of experience as a data scientist and a data-driven business strategy expert; she’s 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
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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