Within a year, you will use machine learning to discover something new and do something better than human beings are doing it now. However, machine learning needs lots of processing and lots of data. The cloud is the fastest and least expensive way to get access and brings sophisticated algorithms, fast computing platforms, and massive storage capacity within the reach of mere mortals.
Easier access has lowered the barrier to adoption; enterprises can now get their feet wet with inexpensive experiments. But easier access has also led to a confusing array of options. As a result, many organizations have little idea what to do with the machine-learning technology readily available on demand.
Jerry Overton provides an executive’s guide to understanding advanced analytics in the cloud, drawing on high-impact data stories from healthcare, manufacturing, and transportation to explain why advanced analytics needs the cloud and how cloud computing is emerging as the future for intelligent machines. Jerry offers a comprehensive survey of cloud technologies, patterns of cloud-based architectures, and patterns of enterprise cloud adoption, describes paths to achieving a cognitive enterprise, and outlines the realistic next steps for executives.
Jerry Overton is a data scientist, distinguished engineer, and head of advanced analytics research at CSC. Jerry is also the chief data scientist for Industrial Machine Learning (a strategic alliance between CSC and Microsoft)—96 enterprise-scale applications across the banking and capital markets, energy and technology, insurance, manufacturing, healthcare, and retail industries. Jerry is the author of Going Pro in Data Science: What It Takes to Succeed as a Professional Data Scientist from O’Reilly. He teaches the Safari Live Online training course Data Science at Enterprise Scale: How to Design and Implement Machine-Learning Solutions That Improve Your Organization. Jerry shares his experiences leading open research and transforming organizations using data science on his blog, Doing Data Science.
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