Summary

The workshop is primarily aimed at programmers (academics, professionals, students) who are familiar with the basics of machine learning and want to learn how to apply it to a real-time environment. Basic familiarity with Java, Python, and machine learning is required, knowledge of Apache Maven (or similar build automation tool) will be helpful, yet is not mandatory. We recommend participants come with some working knowledge of Docker Desktop - covering virtualisation, containers, images, and the Docker Hub.

Syllabus

Architecture of a Flink program
Overview of Flink’s DataStream API
Stateful stream processing
Machine learning in Flink

Instructor

Stefan Popov works as a senior data scientist at Sportradar. Part of the Sports Analytics team, Stefan has more than four and a half years of experience working on various projects within the Integrity, and Regulatory Services divisions. Holding a master’s degree in ICT, Stefan has published several journal and conference papers and has presented his work at international conferences and project meetings.

Attendee equipment prerequisites

It is recommended that you bring a laptop with a working installation of Java, Python, Apache Maven, Apache Flink, Docker Desktop, and your favorite choice of IDE.

Access to all the materials, code, and datasets will be provided to participants a few days before the workshop.

Real-time streaming analytics and machine learning with Apache Flink

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