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Published By: SAS     Published Date: May 17, 2016
This report provides a guide to some of the opportunities that are available for using machine learning in business, and how to overcome some of the key challenges of incorporating machine learning into an analytics strategy. We will discuss the momentum of machine learning in the current analytics landscape, the growing number of modern applications for machine learning, as well as the organizational and technological challenges businesses face when adopting machine learning. We will also look at how two specific organizations are exploiting the opportunities and overcoming the challenges of machine learning as they’ve embarked on their own analytic evolution.
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oreilly, evolution of analytics, sas, machine learning, analytics landscape, networking, it management
    
SAS
Published By: SAS     Published Date: Jun 05, 2017
Analytics is now an expected part of the bottom line. The irony is that as more companies become adept at analytics, it becomes less of a competitive advantage. Enter machine learning. Recent advances have led to increased interest in adopting this technology as part of a larger, more comprehensive analytics strategy. But incorporating modern machine learning techniques into production data infrastructures is not easy.Businesses are now being forced to look deeper into their data to increase efficiency and competitiveness. Read this report to learn more about modern applications for machine learning, including recommendation systems, streaming analytics, deep learning and cognitive computing. And learn from the experiences of two companies that have successfully navigated both organizational and technological challenges to adopt machine learning and embark on their own analytics evolution.
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SAS
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