How to build a serverless electricity price prediction service in Python with Hopsworks + Streamlit

The system we will introduce is an energy price prediction service. We will engineer features in Pandas, use Hopsworks' Python centric API to register features and use features them for training and serving. We will detail the advantages of a Python-based domain specific language (DSL) for point-in-time correct feature selection and skew-free transformations over the alternative SQL approach. Finally we are going build a dashboard making real time predictions using Streamlit

Bio:
Fabio Buso
Fabio Buso is VP of Engineering at Hopsworks, leading the Feature Store development team. Fabio holds a master’s degree in Cloud Computing and Services with a focus on data intensive applications.

===

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/YouTubeVi...