Introduction to Online Machine Learning Algorithms
Online algorithms are an increasingly popular yet often misunderstood branch of machine learning, where model parameter estimates are updated for each new piece of information received. While mini-batch methods have often been mislabeled as 'streaming-machine learning', true online methods have different implementations and goals. This talk will explain key differences between online and offline machine learning, an introduction to many common online algorithms, and how online algorithms can be analyzed. An example using Apache Flink to detect trends on Twitter will be presented. Attendees will come away from this talk with a better understanding of the challenges and opportunities from working with online algorithms and how they can begin implementing their own algorithms in Apache Flink.
This session is a (Intermediate) talk in our IoT and Streaming track. It focuses on Apache Flink, Other and is geared towards Architect, Data Scientist audiences.