Today, no company can survive in the market without using Machine Learning models, and clients will not purchase from companies that do not offer ML-enhanced services. Making a Machine Learning ecosystem operational can help turn enterprise data into a predictive engine for the company. Data and analytics leaders have always understood the benefits of using Machine Learning (ML) for their businesses. The value mostly comes in three ways: operational efficiencies, better top-line growth, and enhanced employee and customer experiences. To unlock that value, however, line-of-business teams must overcome several persistent challenges, with the biggest one being their inability to draw insights from the vast quantities of data they possess.

It's tempting to think of artificial intelligence (AI), cognitive computing and deep learning capabilities as somewhat futuristic--even with companies such as IBM, Microsoft and Google introducing increasingly sophisticated features. Yet machine learning--which constantly sorts through incoming data and improves on its own over time--is already making waves across a wide swath of industries, including travel, pharmaceutical research and financial services. Facebook and Google use machine learning to analyze users, click patterns and deliver personalized content and ads. Others are turning to machine learning and predictive analytics to understand everything from consumer buying and spending patterns to real estate and housing rental markets. Still others are putting it to use to improve cyber-security.