Rating is available when the video has been rented.
This feature is not available right now. Please try again later.
Published on Jul 21, 2016
In the last 10 years, the IT industry has seen a complete revolution in the perceived value that computing has on businesses and how engineers think about applications: in several application domains, the need for data has outgrown the capacity of commodity hardware and the need for information has outpaced traditional processing technologies and approaches. In this talk we'll introduce Apache Flink, a streaming dataflow engine that provides data distribution, communication, and fault tolerance for distributed computations over data streams. It is an open source project that builds on top of proven approaches, as well as innovative algorithms. We will go in-depth on how this tool can be used to implement data-intensive applications, in particular regarding present tools and future perspectives to use machine learning algorithms in a distributed context.
Simone Robutti, 27, Machine Learning Engineer at Radicalbit. He achieved a Master’s Degree at Università degli studi di Milano with a thesis on SVM for noisy labeled datasets. From then on his interests shifted towards the engineering side of Machine Learning and Big Data: implementation, deploy, portability and maintainability of ML-intensive systems. Right now his focus in Radicalbit is Flink and its Machine Learning library FlinkML.