The Feature Store - Jim Dowling

Video Description

PyData London Meetup #56 Tuesday, May 7, 2019 Machine Learning (ML) pipelines are the fundamental building block for productionizing ML code. However, existing tutorials and educational material in Python for Data Scientists emphasizes ad-hoc feature engineering and training pipelines to experiment with ML models. Such pipelines have a tendency to become complex over time and do not allow features to be easily re-used between different ML pipelines. Features used for training and serving may have different implementations that are not consistent. In this talk, we will show how ML pipelines can be programmed, end-to-end, in Python. We will show how a Feature Store can provide a natural interface between Data Engineers, who create reusable features from diverse data sources, and Data Scientists, who experiment with predictive models, built from the same features. In an example end-to-end pipeline in Python, we will show how Python dampens the impedance mismatch between Data Engineering and Data Science, enabling Python to become the only language needed for ML pipelines. Sponsored & Hosted by Man AHL **** www.pydata.org PyData is an educational program of NumFOCUS, a 501(c)3 non-profit organization in the United States. PyData provides a forum for the international community of users and developers of data analysis tools to share ideas and learn from each other. The global PyData network promotes discussion of best practices, new approaches, and emerging technologies for data management, processing, analytics, and visualization. PyData communities approach data science using many languages, including (but not limited to) Python, Julia, and R. PyData conferences aim to be accessible and community-driven, with novice to advanced level presentations. PyData tutorials and talks bring attendees the latest project features along with cutting-edge use cases. 00:00 Welcome! 00:10 Help us add time stamps or captions to this video! See the description for details. Want to help add timestamps to our YouTube videos to help with discoverability? Find out more here: https://github.com/numfocus/YouTubeVideoTimestamps