In order to process large datasets using Hadoop and associated R packages, one needs a cluster of computers. In today's world, it is easy to get using cloud computing services provided by Amazon, Microsoft, and others. One needs to pay only for the amount of CPU and storage used. No need for upfront investments on infrastructure. The top four cloud computing services are AWS by Amazon, Azure by Microsoft, Compute Cloud by Google, and Bluemix by IBM. In this section, we will discuss running R programs on AWS. In particular, you will learn how to create an AWS instance; install R, RStudio, and other packages in that instance; develop and run machine learning models.
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Dr. Hari M. Koduvely is an experienced data scientist working at the Samsung R&D Institute in Bangalore, India. He has a PhD in statistical physics from the Tata Institute of Fundamental Research, Mumbai, India, and post-doctoral experience from the Weizmann Institute, Israel, and Georgia Tech, USA. Prior to joining Samsung, the author has worked for Amazon and Infosys Technologies, developing machine learning-based applications for their products and platforms. He also has several publications on Bayesian inference and its applications in areas such as recommendation systems and predictive health monitoring. His current interest is in developing large-scale machine learning methods, particularly for natural language understanding.
Read more about Hari Manassery Koduvely
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Dr. Hari M. Koduvely is an experienced data scientist working at the Samsung R&D Institute in Bangalore, India. He has a PhD in statistical physics from the Tata Institute of Fundamental Research, Mumbai, India, and post-doctoral experience from the Weizmann Institute, Israel, and Georgia Tech, USA. Prior to joining Samsung, the author has worked for Amazon and Infosys Technologies, developing machine learning-based applications for their products and platforms. He also has several publications on Bayesian inference and its applications in areas such as recommendation systems and predictive health monitoring. His current interest is in developing large-scale machine learning methods, particularly for natural language understanding.
Read more about Hari Manassery Koduvely