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An emerging trend in AI is the availability of automation technologies that train several models and select the one with best-fit. This automated AI process includes several variations of feature engineering and hyperparameter optimization that aim to improve the model. In this lab, you’ll use the Watson Studio AutoAI tool to build a rapid prototype and generate a Python notebook for your prototype. You’ll then examine each of the steps in the Python notebook to see how the AutoAI performed.
Meredith Mante is a Data Scientist on IBM’s AI Learning team. She creates learning experiences in emerging areas like AutoAI. She has a bachelor’s degree in Psychology from Princeton and a master’s degree in Computer Science from NYU. She is passionate about sharing Data Science and AI with a broad community of learners from diverse backgrounds. Outside of work, she enjoys exploring her native New York.
Austin Eovito is a Data Scientist on the Technical Marketing and Evangelism team in San Francisco, California. As a recent graduate student of Florida State University, Austin is focused on the balance of bleeding-edge research produced by academia and the tools used in applied data science. His Masters thesis was on White Collar Crime using Time-aware Joint-Topic-Sentiment Analysis (TTS), and his areas of interests are NLP, applied data science, and Explainable AI. Outside of work, he enjoys spending time with his dog and traveling.