Third Generation Tools for Realizing Machine Learning Algorithms

Data Science Ballroom AB
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Machine learning (ML) algorithms are the cornerstone of Data Science. Traditional ML tools do not scale to large data sets, leading to the next generation tools such as Mahout. However, these tools have implemented only simpler ML algorithms such as linear regression and k-means clustering using Hadoop Map-Reduce (MR) for scaling – this is because of the natural misfit of Hadoop MR for iterative ML algorithms.

The third generation tools such as HaLoop, Twister, Apache Hama and GraphLab provide implementations of iterative ML algorithms and some (GraphLab) even go beyond the MR paradigm. The aim of this session is to give a comprehensive and demonstrative view of the third generation ML tools by taking real-life use cases from Retail and/(or) Healthcare domains.

Photo of Vijay Agneeswaran

Vijay Agneeswaran

Walmart Labs

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 done an internship in Siemens Corporate Research in Bangalore and was with another product development company – Oracle for three years, He subsequently spent a year as principal architect position with GTO, the research arm of Cognizant in Chennai, where he led the Extreme Processing group within the High Performance Computing Centre of Excellence and created Intellectual property in the Big-Data space. He has now taken up the position as Director Technology/Principal Architect as head of the Big-Data R&D at Impetus. He is a professional member of the ACM and the IEEE for the last 7+ years. He has filed patents with US and European patent office’s (with one accepted US patent) and 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.

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