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 the way in which organizations can approach machine learning, making it more accessible to 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 details how you can use AutoML for your own hyperparameter optimization and algorithm selection scenario. If you saw Francesca at the O’Reilly AI Conference in New York, she’s here to give you the latest updates. But you don’t have to have seen her before to benefit. She provides 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
- Learn to 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 a senior machine learning scientist at Microsoft on the cloud advocacy team and an expert in big data technology innovations and the applications of machine learning-based solutions to real-world problems. Her research has spanned the areas of machine learning, statistical modeling, time series econometrics and forecasting, and a range of industries—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. At Harvard, she worked on multiple patent, publication and social network 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 and research institutions around the world. She’s a data science mentor for PhD and postdoc students at the Massachusetts Institute of Technology and speaker at academic and industry conferences—where she shares her knowledge and passion for AI, machine learning, and coding.
Comments on this page are now closed.
Diversity and Inclusion Sponsor
Premier Exhibitor Plus
R & D and Innovation Track Sponsor
For conference registration information and customer service
For more information on community discounts and trade opportunities with O’Reilly conferences
Become a sponsor
For information on exhibiting or sponsoring a conference
For media/analyst press inquires
Are there categories of hyperparameters which are more amenable to being optimized by ML automation?