Deep Learning Market Size is Estimated to Reach USD 415 Billion by 2030

The Global Deep Learning Market is expected to grow at a CAGR of 51.1% from forecast period 2022 to 2030 and expected to reach the value of around USD 415 Billion by 2030.

Deep learning technology has been adopted by many end-user industries and will continue to evolve in the coming years. Deep learning is a branch of machine learning that teaches computers to do what humans do naturally. Currently, this technology has applications in industries such as healthcare, manufacturing, and automotive among others. Deep learning now provides more accurate responses than humans, and as a result, this technology is rapidly spreading around the world. The ability to perform tasks without human intervention is the driving force behind the deep learning market growth. Growth in big data and rising adoption of cloud-based technologies are the two global deep learning market trends that are driving the industry growth.

Many artificial intelligence (AI) applications and solutions rely on deep learning to improve automation by performing physical and analytical tasks without human intervention. The introduction of self-driving technology is a popular example of how deep learning technology will drive market growth. Deep learning tools are experts at extracting structured data from a set of unstructured data without the need for human intervention; this gives deep learning an advantage over machine learning, which requires manual intervention. The growing trend of automation in a variety of industries is expected to boost deep learning market revenue in the coming years.

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Deep Learning Market Regional Outlook

The global deep learning market is divided into five regions: North America, Asia-Pacific, Europe, Latin America, and the Middle East & Africa. In 2021, North America conquered the market with utmost shares and is likely to continue its dominance in the coming years. The growing adoption of deep learning in aerospace & defense, healthcare, automotive, and IT & telecom is expanding the North American deep learning market. The early adoption of technologies increased technological advancements, and the presence of numerous key players in the US and Canada is also supporting the North American deep learning industry. In terms of growth, the Asia-Pacific region is anticipated to attain the fastest growth rate during the projected years. The flourishing smartphones, computers/tablets, and other electronic devices industries are fueling the Asia-Pacific deep learning market. On top of that, increasing the application of robotics, IoT, AI, and machine learning in countries like China, India, Japan, and South Korea will also lead to an impressive growth of the Asia-Pacific industry.

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Deep Learning Market Dynamics

The rapid adoption of computers, smartphones, and other mobile platforms is the primary aspect driving the global deep learning market share. The rising adoption of these devices generates an enormous amount of data. This pool of data is then analyzed by deep learning tools, which helps this technology to make better decisions in any given scenario. Implementation of deep learning in any industry can help save time and money, resulting in a reduction of repetitive tasks conducted by humans. As a result, the growing adoption of deep learning across widespread end-use industries is flourishing the deep learning market value.

Integration of deep learning in big data analytics also supports the market to grow rapidly. Current explosive usage of AI, IoT robotics, industrial automation, cybersecurity, and machine vision technology creates a huge volume of data. Thus the integration of big data with deep learning is anticipated to create significant growth opportunities for the market throughout the projected timeframe from 2022 to 2030. However, lack of technical expertise and rising complexity in hardware owing to the complex algorithm are some of the factors that are limiting the market from growing.

Deep Learning Market Segmentation

The global market is divided into three segments: component, application, end-user, and region.

Based on type, the market is divided into hardware, software, and services. The hardware sub-segment is further divided into the processor (central processing unit (CPU), graphics processing unit (GPU), field programmable gate array (FPGA), and others), memory, and network. The software sub-segment is further split into solution (software framework/SDK) and platform/API. The services sub-segment is categorized as installation, training, and support & maintenance. According to our deep learning industry analysis, the hardware sub-segment generated the largest revenue in type during the forecast timeframe.

The application segment is further split into data mining, signal recognition, image recognition, and others. Based on our analysis, image recognition gathered a significant amount of market share in 2021 and is likely to do so in the coming years.

Furthermore, agriculture, automotive, fintech, healthcare, human resources, law, manufacturing, marketing, retail, and security are the splits of the end-user segment. According to our deep learning market forecast, the security sub-segment is anticipated to occupy a considerable market share from 2022 to 2030. The increasing number of cyber attacks, growing money laundering cases, and advancements in fraud detection are some of the factors boosting the security segment. On the other hand, the healthcare sub-segment is expected to grow with the fastest CAGR in the forthcoming years. Implementation of deep learning in early diagnosis of disease, predicting future hospitalization, and identifying clinical risks are supporting the healthcare sector in deep learning.

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Deep Learning Market Players

Some prominent deep learning companies covered in the industry are Adapteva, Inc., Amazon Web Services (AWS), AMD, Baidu, Fujitsu, General Vision, Graphcore, Google, Huawei Technologies, IBM, Intel, Koniku Micron Technology, Microsoft, Mellanox Technologies, Mythic, Nvidia, Qualcomm, Samsung Electronics, Sensory Inc., Skymind, and Xilinx.

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This release was published on openPR.