Machine Learning Trends - 2021

By NIIT Editorial

Published on 10/02/2021

6 minutes

Machine learning has pushed the limits of Artificial Intelligence and made possible applications that once upon a time seemed too good to be true. Theories are materializing into palpable scenarios to benefit both businesses and customers. Fueled by the pandemic and urged to not be left behind scraping the leftovers of the AI-goldrush, machine learning is being applied in newfound ways to industries left, right, and center. Below we list some of the ways in which 2021 will pan out for machine learning and AI applications across industries. 


Hyper Automation 


The concept was pioneered by Gartner, which advocates that all business processes within the enterprise that can be automated, should be automated. Following the trail of leading corporations who’ve conceptualized the term intelligent process automation is all the rage right in IT now. Automation needs dynamic algorithms, in other words, machine learning & AI, to run the show successfully in the face of volatile, real-time business challenges. ML, deep learning, and robotic process automation are expected to lead the drive towards automating legacy business processes. 


Optimizing AI Engineering 


As per research firm Gartner, only 53% of AI-projects ever make the cut from research to development and then deployment. While transitioning from one phase to another, the challenges that thwart chances of success include scaling operations, maintenance, as well as regulating the entire project. 


Organizations need a bankable framework for such mega-scale undertakings that allows the true potential of AI to emerge without major setbacks. A provisioned AI-engineering process that respects the sanctity of components such as DevOps, DataOps, and ModelOps is expected to find adoption among leading IT corporations working at the vantage point of AI development. 


ML + AI + Cyber Security 


With each year, the intensity and nature of DDoS attacks, malware, and trojan viruses take a new, ominous form giving corporate entities nightmares. The introduction of machine learning and artificial intelligence will make it easier for network operators to timely detect unlawful entries in their systems as well as unattended openings in information security. AI-powered software can compile information from sources like websites, communication systems, transactional gateways not to mention emails, and other miscellaneous external sources. It is expected that with time, machine learning will empower the software to grasp user habits and identify anomalous behaviour in real-time to activate advanced warning systems.   


ML in IoT 


IoT and machine learning share a mutually beneficial relationship that serves their existential purpose to the very end. IoT can push its cradle for efficiency and customization by studying consumer behavior with the help of machine learning algorithms. At the same time, it can generate millions of bytes of vital consumer data that acts as the ideal training set for ML to get better at. The Industrial Internet of Things has the opportunity to multiply production efficiency by feeding multi-sensory information into machine learning algorithms and optimizing each stage of the process.   


As per Transforma Insights, by 2030, the global IoT market will be generating upto $1.5 trillion in revenue. With a view of this finding, it will be one of the most profit-churning sectors for ML and AI as its market size continues to evolve.

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