Are you a data scientist looking to scale your analytics to big data? Are you a big data architect or software engineer looking to benefit from analytics at scale or real-time analytics? Are you a student looking for interesting topics in big data for your thesis?
In this webinar,Dr. Vijay Srinivas Agneeswaran will explore the deficiencies of the Hadoop Map-Reduce paradigm. He will illustrate the reasons why Hadoop MR will be too slow for iterative machine learning applications, the cornerstone of big data analytics. He will also outline the interesting alternatives in this space, starting from the Apache Spark – which has become an important next generation iterative machine learning paradigm. A brief view of Apache Storm for real-time processing will also be given. In addition, an overview of GraphLab for processing large graphs will also be provided.
Date: 25th Feb, 2015
Time: 5:00 PM - 6:00 PM IST
Our Experienced Speaker
Dr. Vijay Srinivas Agneeswaran has a Bachelor’s degree in Computer Science & Engineering from SVCE, Madras University (1998), an MS (By Research) from IIT Madras in 2001 and a PhD from IIT Madras (2008). He was a post-doctoral research fellow in the LSIR Labs, Swiss Federal Institute of Technology, Lausanne (EPFL) for a year. He has spent the last eight years creating intellectual property and building products in the big data area in Oracle, Cognizant and Impetus, where he is now Director and Head, Big Data Labs. He has built PMML support into Spark/Storm and realized several machine learning algorithms such as LDA, Random Forests over Spark. He is also building a big data governance product for a role-based fine-grained access control inside of Hadoop YARN. He is a professional member of the ACM and the IEEE for the last 8+ years. He has two full US patents and has published in leading journals and conferences, including IEEE transactions. His research interests include distributed systems - cloud, grid, peer-to-peer computing as well as machine learning for Big-Data and other emerging technologies.
Language of instruction: English