Abstract: Good machine learning algorithms don’t guarantee good models. Great models need great features. That’s why feature engineering takes so much time. Developing good features is time-consuming, difficult, and most of all requires domain knowledge. Join dotData CEO, Ryohei Fujimaki, PhD, as he discusses the future of Feature Engineering, how automation can help you extract the full-potential of your data, and how you can leverage AI-features to augment and reinforce your AI/ML workflow.
Bio: Aaron is currently the Vice President of Data Science and Solutions at dotData. As a data science practitioner with 14 years of research and industrial experience, he has held various leadership positions in spearheading new product development in the fields of data science and business intelligence. At dotData, Aaron leads the data science team in working directly with clients and solving their most challenging problems.