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Machine learning is generating a huge amount of interest in the technology field, and machine learning engineers who have the right skill sets 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." Accenture research concurs that the impact of AI technologies including machine learning 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.
1. 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.
2. 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.
3. 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.
4. 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.
Also Read: Top Questions For Machine Learning Engineers
Future of 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 hire AI and machine learning engineers for these purposes. ZipRecruiter cites the median salary for Machine Learning engineers in the U.S. at $130,889.
Conclusion:
As machine learning continues its meteoric rise, the demand for skilled engineers with a robust skill set amplifies. Embracing continuous learning, staying updated with emerging technologies, and mastering these top skills will empower machine learning engineers to navigate the challenges and opportunities of 2023 and beyond.
Whether aspiring or seasoned, the path to becoming a proficient machine learning engineer in 2023 involves a commitment to honing these essential skills, driving innovation, and shaping the future of technology.
