The Global Deep Learning Market is Projected to Reach US$60.5 Billion by 2025

The growing AI adoption in customer-centric services is the key factor driving the deep learning market

The global deep learning market is expected to grow from US$12.3 billion in 2020 to US$60.5 billion by 2025 at a compound annual growth rate (CAGR) of 37.5% between 2020 and 2025. The global deep learning market is gaining prominence on account of its complex data-driven applications including voice and image recognition. The rapid increase in the amount of data being generated in different end-use industries is expected to provide traction to the industry growth. Additionally, the increasing need for human and machine interaction is offering new growth avenues to solution providers for providing enhanced solutions and capabilities.

Deep learning, or deep structured learning, is a division of machine learning that uses layered algorithmic models for analysing data. It is a crucial component of data science, which uses statistics and predictive modelling for collecting, analyzing, and interpreting large amounts of information. It also involves the use of artificial intelligence (AI) to imitate the functioning of the human brain while processing data, forming patterns, and making decisions.

The increasing adoption of software solutions in various applications, such as smartphone assistants, ATMs that read checks, voice and image recognition software on social networks, and software that serves up ads on many websites, is driving the growth of machine learning technology in the market. Most companies that manufacture and develop systems and related software provide both online and offline support, depending on the application. Several companies provide installation, training, and support on these systems, along with online assistance and post-maintenance of software and required services.

Another driving factor of market growth is the increasing deployment of smart cities. Deep learning systems are used to build an intelligent infrastructure model, and these systems monitor traffic rate, energy consumption, and take decisions based on the severity of the situation. Deep learning will also be used to manage vast volumes of data generated from different sensors, thus reducing the problem of network congestion.