Speaker: Henry Humadi -- Machine Learning Director, Intelligence Engagement at Leanplum
Title: From notebooks to data products
Henry Graduated from McMaster University/McGill University in Canada with a Ph.D. in computational condensed matter physics. Henry has published multiple articles in peer-reviewed journals and his background in simulation/analysis of physical system has given him the tools to deal with large data sets. In the last five years, he has worked in different data science roles at various Ad-tech/martech companies. Currently, at Leanplum, Henry’s leading a team that focuses on building data products to enable marketers to increase their end user engagement via Machine Learning and A/B testing.
The transition from notebooks and scripts to a full-fledged data product takes a lot of effort. The majority of the issues tend to be engineering in nature. Even with a great data science team, most of the initial gains come from the industry knowledge, simple heuristics, clean and consistent data for insightful analytics, and not from machine learning algorithms. In this talk, we will discuss how to build a data product from scratch and what are key components such as developing a robust data pipeline, data consistency/quality, starting with a simple baseline, analytics/heuristics, and considering scalability problems to name a few. The success of the data science team and driving value from data depend on building a platform that enables experimentation and reduces the friction between the raw data and the business value.
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