Data analysis has come a long way in terms of both the size and the complexity of data. Vartika Singh, Jayant Shekhar, and Jeffrey Shmain walk you through various approaches to unraveling the underlying patterns in data leveraging Spark, machine learning, and related technologies, helping you deal with the noise of the real world and get the maximum value from your data.
Vartika Singh is a solutions architect at Cloudera with over 10 years of experience applying machine-learning techniques to big data problems.
Jayant Shekhar is the founder of Sparkflows Inc., which enables machine learning on large datasets using Spark ML and intelligent workflows. Jayant focuses on Spark, streaming, and machine learning and is a contributor to Spark. Previously, Jayant was a principal solutions architect at Cloudera working with companies both large and small in various verticals on big data use cases, architecture, algorithms, and deployments. Prior to Cloudera, Jayant worked at Yahoo, where he was instrumental in building out the large-scale content/listings platform using Hadoop and big data technologies. Jayant also worked at eBay, building out a new shopping platform, K2, using Nutch and Hadoop among others, as well as KLA-Tencor, building software for reticle inspection stations and defect analysis systems. Jayant holds a bachelor’s degree in computer science from IIT Kharagpur and a master’s degree in computer engineering from San Jose State University.
Jeff Shmain is a principal solutions architect at Cloudera. He has 16+ years of financial industry experience with a strong understanding of security trading, risk, and regulations. Over the last few years, Jeff has worked on various use-case implementations at 8 out of 10 of the world’s largest investment banks.
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