Deep Learning Market: Market Shares, Trends, Top Key Players, Industry Overview and Global Forecast to 2023


Posted October 10, 2018 by nareshkumar

Deep Learning Market by Offering (Hardware, Software, and Services), Application (Image Recognition, Signal Recognition, Data Mining), End-User Industry (Security, Marketing, Healthcare, Fintech, Automotive, Law), and Geography

 
The deep learning market was worth USD 2.28 Billion in 2017 and is expected to reach USD 18.16 Billion by 2023, at a CAGR of 41.7% from 2018 to 2023. The base year considered for this study is 2017, and the forecast period is from 2018 to 2023.

Software to hold largest market share from 2018 to 2023

Software currently holds the largest market share, while the market for services is expected to grow at the highest CAGR between 2018 and 2023. The software segment consists of software frameworks and platforms/APIs developed using algorithms and codes that enable hardware to carry out deep learning programs. Manufacturers and software providers offer different solutions (frameworks/software development kits (SDKs)) and APIs/platforms that are open to developers working on deep learning programs. For example, Qualcomm offers Zeroth SDK, which helps users and developers use Snapdragon 820 capabilities for deep learning applications such as image and sound processing, including speech recognition. The hardware segment consists of processor chips used for running deep learning algorithms based on neuromorphic architecture and/or von Neumann architecture.

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Deep learning market for data mining to grow at highest CAGR from 2018 to 2023

Data mining abstracts related data from files, such as image, video, and audio. With the advent of new technologies, natural language processing and visual data mining have been developed using deep learning techniques. Data mining is used in the following applications: sentiment analysis, machine translation, fingerprint identification, cybersecurity, and bioinformatics. Deep learning offers faster and better memory utilization than traditional computing systems. As data mining is a complex operation, it requires complex hardware architecture and algorithms to perform computational functions, along with service and maintenance of systems. Thus, the demand for services in data mining is expected to grow significantly between 2018 and 2023.

Security to account for largest market size among other end-user industries between 2018 and 2023

Deep learning- and AI-based systems are being significantly used in antivirus and antimalware solutions owing to the rise in cybersecurity attacks across the world. The increasing use of mobile devices for a wide range of applications, such as social networking, emails, remote monitoring, phone banking, and data storage, opens doors for hackers to attack, thereby making networks more vulnerable to risks. The rapid adoption of cloud-based services, along with the user-friendly approach of antivirus/antimalware solutions, is contributing to the growth of this application in the deep learning market for security. The adoption of DL technologies for encryption is likely to witness growth in the coming years.

Target Audience
• Chipset manufacturers
• Cloud service providers
• Commercial banks
• Deep learning/machine learning solution providers
• Device manufacturers
• DL platform providers
• Investors and venture capitalists
• Manufacturers and people implementing AI technology
• Raw material and manufacturing equipment suppliers
• Research organizations, universities, and consulting companies
• Semiconductor companies
• System integrators
• Technology investors
• Technology providers

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The deep learning market comprises hardware manufacturers such as NVIDIA (US), Intel (US), General Vision (US), Graphcore (UK), Xilinx (US), and Qualcomm (US); and deep learning solution providers such as Google (US), Microsoft (US), AWS (US), Sensory Inc. (US), and IBM (US). The other key hardware manufacturers are Samsung Electronics (South Korea), Micron Technology (US), and Mellanox Technologies (Israel).
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Categories Technology
Tags artificial intelligence , deep learning , machine learning
Last Updated October 10, 2018