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Implement Machine Learning for Big Data using Dash & Vaex

Featuring Jovan Veljanoski, Co-Founder of Vaex

Wednesday, August 26th at 2pm EDT

Datasets have become increasingly complex and overwhelming to manage. When faced with Big Data, data scientists turn to Vaex, an open-source DataFrame library in Python specifically designed to work with large datasets using memory mapping. Vaex is the perfect companion to Dash, which uses workers to scale vertically and Kubernetes to scale horizontally. Together, Vaex and Dash empower data scientists to easily create scalable, production-ready web applications. Join Jovan Velijanoski, Co-Founder of Vaex, as he demonstrates how to utilize the scalability of Dash and the superb performance of Vaex to implement Machine Learning and generate actionable business insight.

During this 1 hour webinar, we'll show you how to:
  • Perform data manipulation, aggregation, and statistic computations for Big Data and Machine Learning using Vaex
  • Capture user interactions and automatically update key components by connecting Vaex to Dash
  • Utilize Dash’s stateless, reactive, functional nature to generate interactive, scalable dashboards
  • About the speaker

    Jovan is the Co-Founder of Vaex. Working mostly with Python in the Jupyter/PyData ecosystem, he has considerable experience in creating dashboards, clustering analysis and predictive modeling. Jovan has a PhD in Astrophysics and is also a senior data scientist & researcher at Cloud Technology Solutions, where he creates predictive models and data pipelines for a variety of domains and use cases.

    About the speaker

    Jovan is a senior data scientist & researcher at Cloud Technology Solutions, where he creates predictive models and data pipelines for a variety of domains and use cases. Working mostly with Python in the Jupyter/PyData ecosystem, he has considerable experience in creating dashboards, clustering analysis and predictive modeling. Jovan has a PhD in Astrophysics, is a co-founder of vaex.io, and is interested in novel machine learning technologies and applications.