Abstract: Automated machine learning (AutoML) represents a fundamental shift in the way organizations of all sizes approach machine learning and data science. Applying traditional machine learning methods to real-world business problems is time-consuming, resource-intensive, and challenging. It requires experts in the several disciplines, including data scientists – some of the most sought-after professionals in the job market right now.
Automated machine learning changes that, making it easier to build and use machine learning models in the real world by running systematic processes on raw data and selecting models that pull the most relevant information from the data – what is often referred to as “the signal in the noise.” Automated machine learning incorporates machine learning best practices from top-ranked data scientists to make data science more accessible across the organization.
Please join us for this hands-on training and learn how you can use the DataRobot automated machine learning platform that enables users of all skill levels, from business people to analysts to data scientists, to build and deploy highly-accurate predictive models in a fraction of the time of traditional modelling methods.
Bio: Qian Zhao is the Customer Facing Data Scientist from DataRobot based in London. Qian received his Ph.D. degree in Neuroscience from University College London. After working as a lead data scientist and data science manager in both consultancy and industry for 10 years, he joined DataRobot in 2018. In his current position, Qian works on both open source tools and automated machine learning platform to implement machine learning models that solve complex business problems and drive real ROI.
Qian Zhao, PhD
Customer Facing Data Scientist | DataRobot
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