Theodore Vasiloudis

As machine learning (ML) finds its way into more and more areas in our life, software developers from all fields are asked to navigate an increasingly complex maze of tools and algorithms to extract value out of massive datasets. In this talk we'll try to help the aspiring ML developer by describing:

• a conceptual framework that most ML algorithms fall under
• considerations about data readiness, algorithms, and software tools from an open-source perspective
• some common mistakes and misconceptions in the development and deployment of ML systems

The goal of the talk is to aid the audience to think about ML problems in an integrated manner; facilitating the process of going from problem to prototype, making an informed choice about the algorithms and software to use, and providing examples of issues that can, and do come up in production.

The talk is designed to be informative and entertaining, with little previous knowledge required.

Apache Flink is an open source platform for distributed stream and batch data processing. In this talk we will show how Flink's streaming engine and support for native iterations make it an excellent candidate for the development of large scale machine learning algorithms.

This talk will focus on FlinkML, a new effort to bring scalable machine learning tools to the Flink community. We will provide an introduction to the library, illustrate how we employ some state-of-the-art algorithms to make FlinkML truly scalable, and provide a view into the challenges and decisions one has to make when designing a robust and scalable machine learning library.

Finally, if time permits, we will demonstrate how one can perform some interactive analysis using FlinkML and the notebook environment of Apache Zeppelin.