市場調査レポート
商品コード
1094388
ディープラーニングの世界市場予測(~2028年):ソリューション、アーキテクチャ産業、地域別の分析Deep Learning Market Forecasts to 2028 - Global Analysis By Solution (Hardware, Software, Services), Architecture Industry (Recurrent Neural Network (RNN), Convolutional Neural Networks (CNN)) and By Geography |
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ディープラーニングの世界市場予測(~2028年):ソリューション、アーキテクチャ産業、地域別の分析 |
出版日: 2022年06月01日
発行: Stratistics Market Research Consulting
ページ情報: 英文 200+ Pages
納期: 2~3営業日
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世界のディープラーニングの市場規模は、2021年に94億5,000万米ドルとなり、予測期間中に43.7%のCAGRで拡大し、2028年までに1,196億1,000万米ドルに達すると予測されています。
当レポートでは世界のディープラーニング市場を調査し、市場の促進要因・抑制要因、市場機会、COVID-19の影響、セグメント別の市場分析、競合情勢、主要企業のプロファイルなど、体系的な情報を提供しています。
Table17 Global Deep Learning Market Outlook, By Recurrent Neural Network (RNN) (2020-2028) (US $MN)
Note- Tables for North America, Europe, APAC, South America, and Middle East & Africa Regions are also represented in the same manner as above.
According to Stratistics MRC, the Global Deep Learning Market is accounted for $9.45 billion in 2021 and is expected to reach $119.61 billion by 2028 growing at a CAGR of 43.7% during the forecast period. Deep learning is a type of machine learning and artificial intelligence (AI) that imitates the way humans gain certain types of knowledge. Deep learning is an important element of data science, which includes statistics and predictive modeling.
An increase in digitalization along with the development of the information technology (IT) industry across the globe is one of the major factors driving the growth of the market. Deep learning algorithms are proficient in inevitably intercepting available data points that improve the accuracy and efficiency of the decision-making process. The increase in the number of cyber-attacks encouraging industries to employ database management, fraud detection systems, and cyber security accelerate the market. This technology is used for processing medical images for drug discovery, and disease diagnosis delivering virtual patient assistance in the healthcare sector.
In contrast to traditional data analysis, deep learning demands a totally diverse set of technical skills and expertise. There are an inadequate number of specialists to provide the required expertise in business problems and organizations that have budget constraints have to shy away from hiring the right talent to fulfil the needs. Besides, it is time-consuming for organizations to find well-trained professionals with appropriate skill sets. Thus, due to the shortage of expertise is limiting the implementation of deep learning models restricting the market growth.
With an upsurge in funding through numerous global investors, the global market has witnessed an intrusion of start-ups in recent years. The major start-up sector that offers global deep learning is healthcare, which is focused on drug research and development. Other areas of application in deep learning are visual recognition, fraud detection, insurance, and agriculture. This gives opportunities to vendors to increase their market shares and attract customers from a wide range of industries. Thus, the rise in start-ups and deep learning applications in several industries creates ample opportunity.
The quality of data remains to be one of the biggest factors as models like deep learning need a lot of quality data. With small enough datasets, an algorithm may be taught without being inclusive. This is very much required for processes like image recognition; without accurate and adequate data, it becomes a fairly uphill task for the deep learning model to reach the next stage and ensure a greater grasp in the market. Such errors can lie undiscovered for a long time and correcting them can take much longer. Rigid business models also limit the revenue growth of the market. However, not all corporations are flexible in their business process and do not allow experimentation which limits the revenue growth of the market.
The hardware segment dominated the market, owing to the increasing requirement for hardware platforms with high computing power to implement deep learning algorithms. The hardware segment comprises processors such as GPU, FPGA, and CPU among others, memory, and network. The rapidly evolving R&D activities for the expansion of better processing hardware for deep learning are also accelerating the market value.
The recurrent neural networks (RNN) segment held the highest market share. As recurrent Neural Networks (RNN) is a powerful and vigorous type of neural network and belong to the most capable algorithms at the moment, as they are the only ones with internal memory. Due to their internal memory, RNNs can remember significant things about the input they received, which allows them to be very accurate in predicting what's coming next.
The North America is projected to hold the highest market share, owing to growing funding in artificial intelligence and neural networks and the province's widespread use of image and monitoring purposes is estimated to generate new growth prospects over the forecast period. Moreover, upsurge in investments in deep learning start-ups and a surge in popularity of deep learning technology among end-users. Additionally, the province is one of the pioneers of modern technologies, allowing firms to accelerate the adoption of deep learning ability.
Asia Pacific is projected to have the highest CAGR, due to the rapid economic development of key nations such as China and India is important to encourage the growth of the Asia Pacific deep learning market. The growing penetration and development of deep learning technology are the driving forces behind the market's growth. Additionally, the spurring rise of digitization and image and voice recognition platforms is giving a boost to the growth of the market. Moreover, foreign investments in model applications of deep learning favor the growth of the regional market.
Some of the key players profiled in the Deep Learning Market include Advanced Micro Devices, Inc., Amazon Web Services (AWS), ARM Ltd., Clarifai, Inc., Entilic, Google, IBM, Hewlett Packard Enterprise, HyperVerge, IBM Corporation, Intel Corporation, Micron Technology, Microsoft Corporation, NVIDIA Corporation, Qualcomm Technologies, Inc , Samsung Electronics, Johnson Controls, Larsen & Toubro Infotech.
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