Machine and deep learning disciplines are generating a huge amount of interest in the technology field, and data science professionals who have the right machine learning skills and deep learning skills will be well-positioned to excel in the coming years. Revenues for enterprise applications that leverage artificial intelligence (AI) technologies, including its sub-segments machine learning and deep learning, are projected to skyrocket more than 50 percent per year to $37.9 billion by 2024. Even Google’s CEO Sundar Pichai recently made the bold statement, “AI is probably the most important thing humanity has ever worked on. I think of it as something more profound than electricity or fire." Wow! Accenture research concurs that the impact of AI technologies on businesses is projected to increase labor productivity by up to 40 percent and could double economic growth rates by 2035 by changing the nature of work and creating new relationships between man and machine.

The prospects for businesses that leverage AI are exciting, and companies are quickly ramping up their workforces to take full advantage of the benefits AI, deep learning, and machine learning will bring. The advanced skill sets needed to master these technologies are in growing demand, with the share of jobs requiring AI skill sets expanding 4.5 times since 2013. The following is a quick overview of various technological skills, evolving job prospects, and market drivers that will personify this revolution in intelligent machine thinking.

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Deep Learning Skills and TensorFlow

Data science has always been focused on analyzing massive amounts of data – both inside and outside the enterprise – to derive business benefits. Specialties are now emerging in the data science field that leverages neural networks to make analysis faster, more accurate, and smarter. Neural networks are built on machine learning algorithms to create an advanced computation model that works much like the human brain. One of the most popular software platforms used for deep learning is TensorFlow, the open-source software library that Google developed to conduct machine learning and deep neural networks research. Deep learning models that use TensorFlow are being used in everything from healthcare, and improving agricultural yields to helping find solutions to climate change, increasing the demand for deep learning skills in the process. 

Natural Language Processing

Natural language processing (NLP) in the field of computer science and AI concerned with understanding and processing the interactions between computers and natural human language. Specialists leverage NLP technologies to efficiently process natural language data on a vast scale, using analysis to perform tasks such as improving speech recognition, which has dramatic implications across a wide range of industries. Along with machine learning and deep learning, natural language processing is one of the most in-demand skills.

Robotic Process Automation

Robotic process automation (RPA) is the application of technology that allows technicians to configure computer software or a “robot” to capture and interpret existing applications for processing a transaction, manipulating data, triggering responses, and communicating with other digital systems. The global market for RPA software and services reached $271 million in 2016 and is expected to grow to $7.64 billion by 2028. 

Don’t Forget Core Data Science Skills

Highly in-demand skills include traditional Big Data analytics and data science fields, including Python, Java, C++, experience with open source development environments, Spark, MATLAB, and Hadoop. These skills form the foundation for AI expertise and produce great career prospects: ZipRecruiter cites the median salary for Machine Learning occupations in the U.S. at $130,889.

Career Benefits Include Contractors and Freelancers Too

The demand for AI and machine learning skills is so high that companies must also turn to contractors and freelancers to fill the skills gaps in Machine Learning and other AI segments. It has been reported that AI and related fields, such as natural language processing, were imperative. 

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Top Markets for AI, Machine and Deep Learning

Adoption of AI, machine learning, and deep learning technologies is accelerating across a wide range of industries with the inclusion of more professionals with required Machine Learning skills. In fact, the human relations (HR) business is one of those industries riding the AI wave quite effectively. HR and recruiting departments are tasked with sifting through vast numbers of resumes, and new platforms such as HiringSolved and Entelo offer AI tools that help match candidates with open positions. Machine learning can also be used to help craft job descriptions that are free of biased language or even manage repetitive tasks, such as scheduling candidate interviews. Significant changes in financial services are also expected thanks to machine learning, which executes huge volumes of trades more efficiently so that human agents can focus on the more important relationship-building activities with clients. And, as AI applications get better at making intelligent real-time predictions, companies are using them to improve the customer experience. Tech Times estimates that 90 percent of early-stage startups they work with are planning to use AI and machine learning for these purposes.

Check out the Simplilearn video on "Machine Learning vs Deep Learning vs Artificial Intelligence" delivered by our industry experts that helps you understand the difference between machine learning, deep learning, and Artificial Intelligence.

No matter what industry you’re currently in, the odds are pretty good that AI, machine learning, and deep learning technologies will be impacting your job soon, if they haven’t already. Raising the machine learning skill sets of your technology teams to keep up with these groundbreaking trends will enhance their ability to remain competitive in the new AI-driven world.

About the Author

Stuart RauchStuart Rauch

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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