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Upcoming events (3)
Deciphering the Black Box: Understanding your Machine Learning Model by Rajiv Shah This talk will show how interpretability tools can give you not only more confidence in a model, but also help to improve model performance. This workshop will cover best practices for using techniques such as feature importance, partial dependence, and explanation approaches, such as LIME. Along the way, we will consider issues like spurious correlation, multicollinearity, and other issues that may affect model interpretation and performance. The talk will use easy to understand examples and references to open source algorithms to illustrate the techniques. Rajiv Shah is a data scientist with DataRobot and an Adjunct Assistant Professor at the University of Illinois at Chicago. He has previously worked as a data scientist for State Farm and Caterpillar. He is an active member of the data science community in Chicago. He has a PhD from the University of Illinois at Urbana Champaign.
Chicago ML is delighted to announce our first collaboration with IBM on May 30! Open Standards for Machine Learning Model Deployment by Svetlana Levitan Dr. Svetlana Levitan, Developer Advocate with Center for Open Source Data and AI Technologies (CODAIT) at IBM, discuses open standards that have made the process of model deployment easier. Predictive model deployment is the part of the machine learning process where the practical results are achieved, when the model is used for generating predictions on new data (known as scoring). The deployment used to present big difficulties, as models were typically built in one environment and needed to be deployed in a different one. Often they would need to be re-implemented in a new programming language, that would be very slow and error-prone. Predictive Model Markup Language (PMML) and Portable Format for Analytics (PFA) were developed by the Data Mining Group (DMG) that originated in Chicago. PMML has been around for more than 20 years and is used widely. PFA is an emerging standard that is getting a lot of interest. Open Neural Network eXchange (ONNX) format was recently developed by Facebook and Microsoft as a way to exchange deep learning (DL) models between different DL frameworks, and is now experiencing explosive growth. Attendees will get a good understanding of predictive model deployment challenges and approaches. Svetlana Levitan, PhD, is Developer Advocate with Center for Open Source Data and AI Technologies (CODAIT) at IBM. She has been a software engineer, architect, and technical lead for SPSS Analytic components for many years. She represents IBM at the Data Mining Group and is the release manager for PMML and PFA, open standards for predictive model deployment. She is also working with other companies on ONNX, an open model exchange format for deep learning models. Svetlana is a co-organizer of several Chicagoland Meetup groups, including Big Data Developers in Chicago, Chicago Cloud Developers, Open Source Analytics. She has authored several blogs and presented at many conferences and other events. Svetlana loves to learn new technologies and to share her expertise, to encourage girls and women in STEM.
Chicago ML is extremely excited to announce our first meetup with the ML team at Fermilab - America's particle physics and accelerator laboratory! We will be hearing from: Gabe Perdue - Deep learning for ghost particles Joao Calderia - Machine learning the universe Javier Duarte - The fastest inference at the highest energies Some of the group's ML papers: https://arxiv.org/abs/1902.00743 https://arxiv.org/abs/1808.08332 https://arxiv.org/abs/1902.10159 https://arxiv.org/abs/1812.01106 https://arxiv.org/abs/1810.01483 https://arxiv.org/abs/1904.08986 https://arxiv.org/abs/1804.06913 https://arxiv.org/abs/1807.02876 Please note the meetup is at Fermilab in Batavia, Illinois - approximately 40 miles west of Chicago. We'll organize car pools from Chicago as we near the date.