Deep learning, considered as a subfield of Machine Learning, is concerned with algorithms and largely inspired by the brain’s structure and a function called the artificial neural network. Technology is advancing at an alarming pace, and the latest advancements in artificial intelligence (AI) are nothing short of overwhelming. Deep learning is gaining momentum in AI owing to its supremacy in terms of accuracy when trained with large volumes of data. The current era deals with big data, which is estimated to provide significant opportunities for new innovations in deep learning.
Deep learning needs high-end machines, contrary to traditional machine learning algorithms. In addition, in traditional machine learning techniques, a majority of the applied features need to be identified by a domain expert in order to reduce data complexity and make patterns more visible to learning algorithms to work. However, deep learning learns high-level features from data in an incremental manner, which eliminates the requirement for domain expertise as well as hardcore feature extraction. Deep learning is also known as deep neural learning or deep neural network.
Nowadays, big data is extensively adopted by various business organizations as they are collecting a significant amount of data according to the requirements of the organization. This data generation is estimated to increase further with upcoming technologies such as 5G. Therefore, it is estimated that deep learning will find applications in big data analytics in order to extract sophisticated patterns from a large amount of data. Deep learning can learn and assess a significant amount of unsupervised data; hence, it is considered as an appropriate tool for big data analytics. The increasing demand for big data analytics is estimated to further foster the deep learning market growth.
Nowadays, competition in sectors is intensifying, and players are implementing various strategies to understand customer behavior. Customized products and services are gaining traction today; hence, companies are adopting artificial intelligence to collect and handle data regarding customer requirements and preferences. By doing so, they are able to apply tailor-made offerings and provide a personalized shopping experience. Online shopping sites and social media platforms also provide customized notifications for each and every user.
AI-enabled with deep learning can analyze massive volumes of customer data within seconds. It also provides insights regarding previous shopping history and analyses customer choices. Through these techniques, players can also understand customers’ price preferences. The escalating use of artificial intelligence in customer data analysis is anticipated to create ample opportunities for the global deep learning market.
On the basis of the solution, the market is classified into software, hardware, and services. The software segment dominates the market owing to the rising adoption of the Software as a Service model due to its cost-effectiveness and user-friendliness.
On the basis of hardware, the deep learning market is classified into a central processing unit, graphics processing unit, field-programmable gate array, an application-specific integrated circuit. The graphics processing unit segment holds the lion’s share of the market and is expected to remain dominant throughout the forecast period. As compared to other chipsets, GPUs offer supreme performance, which is the key factor propelling their adoption. Furthermore, rising R&D activities in the development of GPU chipsets are also positively influencing demand.
On the basis of application, the global deep learning market is categorized into image recognition, voice recognition, video surveillance and diagnostics, and data mining. The image recognition segment accounts for the most prominent value share among all, owing to the extensive application of image recognition technology in social media platforms such as Facebook. Furthermore, increasing adoption of image recognition in the defense and healthcare verticals is envisaged to propel the industry growth in the coming years.
Automotive, aerospace and defense, healthcare, manufacturing, and marketing are among the key industries that use deep learning. The aerospace and defense sector dominates the market attributed owing to the widespread application of deep learning in object detection and localization, remote sensing, identifying network anomalies, spectrogram analysis, and malware detection. The industry is exploiting the technology for data-driven analysis by processing massive volumes of data. In addition to this, the technology is also leveraged for image processing and data mining to analyze the future course of action. Deep learning is used by the U.S. Department of Homeland Security to examine forthcoming events in its Synthetic Environment for Analysis and Simulations (SEAS) project.
The marketing segment is expected to register the highest CAGR of xx% owing to the popularity of deep learning in the field of marketing, mainly for media and advertising. Here, the key applications include social media advertising, search advertising, and sales and marketing automation. Deep learning helps enterprises understand customer behavior and preference, which is essential in marketing.
North American dominates in the global deep learning market owing to the increasing demand for deep learning applications, including image recognition, data mining, and signal recognition. Deep learning has paved the way for significant improvements in image recognition in terms of accuracy. Key players in the region are increasing investments in deep learning technology. Companies such as Google and Facebook have established research laboratories and acquired deep learning start-ups since 2013. Google started using deep learning in 47 services, including image recognition and speech recognition, in March 2017. Furthermore, the region is an early adopter of advanced technologies, which broadens the adoption of deep learning at a faster pace.
Key players are relying on strategic mergers and acquisitions to enhance their market penetration. For instance, after the acquisition of Nervana Systems in 2016, Intel introduced Intel Nervana Neural Network Processor, the first silicon for a neural network processor. The technology helps end-user companies build new sets of AI applications that maximize the amount of processed data. It also provides insights and helps companies transform their business with deep learning technology.
Some of the key players in the global deep learning market include NVIDIA, Samsung Electronics, Intel Corporation, Xilinx, Qualcomm, Micron Technology, IBM, Google Inc., Microsoft Corporation, and Amazon Web Services. The market also has start-ups such as Mythic, Graphcore, Adapteva, and Koniku operating globally.
|Market Size||USD in Billion By 2030|
|Forecast Units||Value (USD Million)|
|Report Coverage||Revenue Forecast, Competitive Landscape, Growth Factors, Environment & Regulatory Landscape and Trends|
|Segments Covered||by Solution (Hardware, Software, Services), Hardware (CPU, GPU, FPGA, ASIC), Application, End User|
|Geographies Covered||North America, Europe, Asia-Pacific, LAME and Rest of the World|
|Key Companies Profiled/Vendors||NVIDIA, Samsung Electronics, Intel Corporation, Xilinx, Qualcomm, Micron Technology, IBM, Google Inc., Microsoft Corporation, and Amazon Web Services. The market also has start-ups such as Mythic, Graphcore, Adapteva, and Koniku operating globally.|
|Key Market Opportunities||Rapid Growth In Technology Industry Enhances Deep Learning Market|