AI and Deep Learning Put Big Data on Steroids

AI and Deep Learning Put Big Data on Steroids
Author

Stuart Rauch

Last updated March 19, 2018


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There’s a major transformation taking place in the Big Data and data science fields, and it’s catching the attention of data-driven organizations everywhere. New tools are being developed that leverage artificial intelligence (AI), machine learning and the growing field of deep learning to bring data analysis to a new level of effectiveness. As a discipline, data science focuses on utilizing vast amounts of data to make better business decisions. But if you can enhance the quality of the core data itself, your analysis will be that much more precise—and that much more valuable. AI, machine learning, and deep learning do just that—they unearth and clarify new patterns in data that essentially put Big Data on steroids, and open new opportunities to leverage that data for business gain. With the market for enterprise AI systems projected to increase from $202.5 million in 2015 to $11.1 billion by 2024, it’s a trend that will continue to create excitement in the data science sector. 

So What Are AI, Machine Learning, and Deep Learning?

AI has come a long way from being perceived as a phenomenon of science fiction. In fact, AI has now taken hold in the business and technology worlds and is anything but fiction. Fundamentally, AI represents techniques that enable computers to mimic human intelligence, using logic, if-then rules, decision trees and other intelligent functions. Drill down into AI a bit further and you get a subset called machine learning, which makes use of statistical techniques that empower machines to perform tasks better through repeated experiences. Finally, deep learning is yet another subset within machine learning composed of algorithms that permit software to train itself to perform tasks by exposing multi-layered neural networks to vast amounts of data (i.e. we train the neural networks to learn). Examples of deep learning are speech and image recognition technologies that are commonplace across industries today. 

Want to know more? Here's a video on ' Machine Learning vs Deep Learning vs Artificial Intelligence.'

 

AI and Deep Learning Tools Drive Better Data Science

Data scientists will always be in high demand because of their ability to turn data into business action. With AI and deep learning, the focus becomes less on the front-end analytical side of the equation, and more about extracting the most out of the data on the back end by using very specialized intelligent tools. A Gartner study reports that 80 percent of data scientists will have deep learning in their toolkits this year, and by 2019, deep learning will be a critical driver for best-in-class performance for demand, fraud and failure predictions. Deep learning is adding tremendous value to the field of predictive analytics, uncovering intelligence within the data that used to be much harder to ascertain. Intelligent deep learning tools are built to evaluate and improve efficiencies and analyses on their own, and they are having a particularly big impact in the medical field, for example, improving the quality of analysis for patient diagnoses. 

Robotic Process Automation (RPA) Creates Continuous Improvement

The next logical stage in the evolution of the AI industry is to apply advanced intelligence to drive better business processes. RPA allows data scientists to configure computer software or a “robot” to capture and interpret existing applications for processing transactions, manipulating data, triggering responses and communicating with other digital systems. RPA-based bots are designed to automate easy tasks and make broad data sources accessible to AI, which in turn learns to mimic and improve the processes based on data received from the RPA. The global market for RPA software and services reached $271 million in 2016 and is expected to grow to $1.2 billion by 2021 at a compound annual growth rate of 36 percent. RPA tools such as Blue Prism are among the most popular ways to manage human-to-robot interactions. 

How to Upskill Your Workforce for Deep Learning

In the short term, organizations can turn to a variety of packaged AI tools and APIs to conduct machine learning and deep learning activities. But over the long haul, it will be the presence of a highly trained workforce that will determine whether your organization succeeds in leveraging the deep learning opportunity. Deep learning courses that focus on critical frameworks such as TensorFlow, for example, can provide essential deep learning expertise that can be applied to improve data and business analysis across the organization. On a more general level, there are a wealth of courses on broader AI and machine learning technologies that provide a solid foundation on which to build your team’s expertise. AI engineers, in particular, are in high demand given the scarcity of qualified professionals in this growing segment. 

The market for AI, machine learning, and deep learning are just getting revved up. At their core, they will have a dramatic impact on Big Data and data science functions, offering deeper and more intelligent insights into data that will transform virtually all areas of businesses—and of our daily life. 

Find our Deep Learning with TensorFlow Online Classroom training classes in top cities:

Name Date Place
Deep Learning with TensorFlow 29 Sep -28 Oct 2018, Weekend batch Your City View Details
Deep Learning with TensorFlow 27 Oct -25 Nov 2018, Weekend batch Dallas View Details
Deep Learning with TensorFlow 24 Nov -23 Dec 2018, Weekend batch San Francisco View Details

About the Author

Stuart Rauch is a 25-year product marketing veteran and president of ContentBox Marketing Inc. He has run marketing organizations at several enterprise software companies, including NetSuite, Oracle, PeopleSoft, EVault and Secure Computing. Stuart is a specialist in content development and brings a unique blend of creativity, linguistic acumen and product knowledge to his clients in the technology space.


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