Deep Learning Market is forecast to reach $18.16 billion by 2023 from $3.18 billion in 2018 at a CAGR of 41.7% during (2018-2023) driven by improving computing power, declining hardware cost, the increasing adoption of cloud-based technology, usage in big data analytics and growing AI adoption in customer-centric services, according a report by MarketsandMarkets. Deep learning is a variety of machine learning (ML).
By vertical, deep learning in manufacturing is expected to show the most growth through 2023. Deep learning technology is used in industrial robots, machine vision systems and more to improve the process and product quality, minimize cycle time, and increase efficiency of the manufacturing process as a whole.
Rapid growth also is expected for deep-learning services. Deep-learning systems require installation, training and support, and maintenance services. Installation services allow the software to be integrated with the analytics side to help with data retrieval and to generate the desired result through computation. The use of computer systems for deep learning/AI further increases the amount of work involved in installation.
The companies that are profiled in the deep learning market report are NVIDIA (US), Intel (US), Xilinx (US), Samsung Electronics (South Korea), Micron Technology (US), Qualcomm (US), IBM (US), Google (US), Microsoft (US), and AWS (US). Some of the key start-ups included in this report are Graph core (UK), Mythic (US), Adapteva (US), and Koniku (US).
The deep learning market in APAC expected to grow at highest CAGR. This report covers the deep learning market in North America, Europe, APAC, and RoW. Rise in the adoption of deep learning technology in APAC could be attributed to the increasing applications of deep learning in media & advertising, finance, and retail sectors, among others, in technologically advancing countries such as India, China, and Japan. Growing e-commerce, online streaming, and increasing internet penetration have resulted in the growth of marketing industries. In the security vertical, with increasing incidents of cyber-attacks and a growing cyber-war in the region, organizations and governments are focusing on robust defense infrastructure.
North America accounts for a substantial share of the deep-learning market; however, the lack of technical expertise, absence of standards and protocols, and increasing complexity in hardware due to the complex algorithm used in deep-learning technology are restraining the growth of the market, MarketsandMarkets said.
Related Reading - 10 Alarming Predictions for Deep Learning in 2018
The deep learning market for manufacturing industry will witness highest growth between 2018 and 2023. Deep learning technology is used in industrial robots, machine vision systems, and others to improve the process and product quality, minimize cycle time, and increase the efficiency of the manufacturing process as a whole. North America accounts for a substantial share of the deep learning market, with the US being the major contributor. However, the lack of technical expertise and absence of standards and protocols, and increasing complexity in hardware due to complex algorithm used in deep learning technology are restraining the growth of the deep learning market.
The Deep Learning Market for services to grow at highest CAGR from 2018 to 2023. Deep learning technology is highly complex in nature requiring the implementation of sophisticated algorithms. Deep learning systems require installation; training; and support and maintenance services. Installation services allow the software to be integrated with the analytics side to enable data retrieval and generate desired result through computation. The use of computer systems for DL/AI further increases the amount of work involved in installation.
In terms of hardware, processor held the largest size of the deep learning market in 2017. Companies in industries such as healthcare and finance are investing in machine learning infrastructure. High parallel processing capabilities and improved computing power have resulted in the high adoption of GPUs in various DL applications.
The above press release was first published in Channel Partners.
According to market research firm Gartner, Deep learning will be a critical driver for demand, fraud and failure predictions by 2019. ML through deep learning is increasingly being used in predicting demand, determining deficiencies around service and product quality, detecting new types of fraud, streaming analytics on data in motion and providing predictive or even prescriptive maintenance.
Earlier this year, Chinese firm Alibaba’s iDST (Institute of Data Science of Technologies), Alibaba Group’s research arm focused on artificial intelligence, developed a deep-learning model that scored higher than a human being on a Stanford University reading-comprehension test. Notably, that was the first time that a machine has outperformed humans on such a test.
By vertical, deep learning in manufacturing is expected to show the most growth through 2023. Deep learning technology is used in industrial robots, machine vision systems and more to improve the process and product quality, minimize cycle time, and increase efficiency of the manufacturing process as a whole.
Rapid growth also is expected for deep-learning services. Deep-learning systems require installation, training and support, and maintenance services. Installation services allow the software to be integrated with the analytics side to help with data retrieval and to generate the desired result through computation. The use of computer systems for deep learning/AI further increases the amount of work involved in installation.
The companies that are profiled in the deep learning market report are NVIDIA (US), Intel (US), Xilinx (US), Samsung Electronics (South Korea), Micron Technology (US), Qualcomm (US), IBM (US), Google (US), Microsoft (US), and AWS (US). Some of the key start-ups included in this report are Graph core (UK), Mythic (US), Adapteva (US), and Koniku (US).
The deep learning market in APAC expected to grow at highest CAGR. This report covers the deep learning market in North America, Europe, APAC, and RoW. Rise in the adoption of deep learning technology in APAC could be attributed to the increasing applications of deep learning in media & advertising, finance, and retail sectors, among others, in technologically advancing countries such as India, China, and Japan. Growing e-commerce, online streaming, and increasing internet penetration have resulted in the growth of marketing industries. In the security vertical, with increasing incidents of cyber-attacks and a growing cyber-war in the region, organizations and governments are focusing on robust defense infrastructure.
North America accounts for a substantial share of the deep-learning market; however, the lack of technical expertise, absence of standards and protocols, and increasing complexity in hardware due to the complex algorithm used in deep-learning technology are restraining the growth of the market, MarketsandMarkets said.
Related Reading - 10 Alarming Predictions for Deep Learning in 2018
The deep learning market for manufacturing industry will witness highest growth between 2018 and 2023. Deep learning technology is used in industrial robots, machine vision systems, and others to improve the process and product quality, minimize cycle time, and increase the efficiency of the manufacturing process as a whole. North America accounts for a substantial share of the deep learning market, with the US being the major contributor. However, the lack of technical expertise and absence of standards and protocols, and increasing complexity in hardware due to complex algorithm used in deep learning technology are restraining the growth of the deep learning market.
The Deep Learning Market for services to grow at highest CAGR from 2018 to 2023. Deep learning technology is highly complex in nature requiring the implementation of sophisticated algorithms. Deep learning systems require installation; training; and support and maintenance services. Installation services allow the software to be integrated with the analytics side to enable data retrieval and generate desired result through computation. The use of computer systems for DL/AI further increases the amount of work involved in installation.
In terms of hardware, processor held the largest size of the deep learning market in 2017. Companies in industries such as healthcare and finance are investing in machine learning infrastructure. High parallel processing capabilities and improved computing power have resulted in the high adoption of GPUs in various DL applications.
The above press release was first published in Channel Partners.
According to market research firm Gartner, Deep learning will be a critical driver for demand, fraud and failure predictions by 2019. ML through deep learning is increasingly being used in predicting demand, determining deficiencies around service and product quality, detecting new types of fraud, streaming analytics on data in motion and providing predictive or even prescriptive maintenance.
Earlier this year, Chinese firm Alibaba’s iDST (Institute of Data Science of Technologies), Alibaba Group’s research arm focused on artificial intelligence, developed a deep-learning model that scored higher than a human being on a Stanford University reading-comprehension test. Notably, that was the first time that a machine has outperformed humans on such a test.
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