[299 Pages Report] Global deep learning demand is anticipated to be valued at US$ 12,569.0 Million in 2022, forecast to grow at a CAGR of 26.4% to be valued at US$ 130,667.0 Million from 2022 to 2032. Growth of the Deep Learning Market is attributed to a rapid adoption of cloud-based technology across several industries.
Deep learning is evolving as one of the most advanced technologies in enterprise computing. Organizations are using deep learning neural networks to obtain valuable insights from vast amounts of data to offer innovative products and improved customer experience and thereby increasing revenue opportunities for the deep learning market.
The rising need for enhanced human and system interaction will be driving the growth of the deep learning market during the forecast period. As deep learning systems offer expert assistance, it will assist humans to extend their capabilities. Also, there is a rise in usage of deep learning technology across various industrial verticals such as medical, finance, automotive retail, and others. Another driving factor for this market is the robust R&D for the expansion of better processing hardware for deep learning.
| Data Points | Key Statistics |
|---|---|
| Growth Rate (2016-2021) | 20.2% CAGR |
| Expected Market Value (2022) | US$ 12,569.0 Million |
| Anticipated Forecast Value (2032) | US$ 130,667.0 Million |
| Projected Growth Rate (2022-2032) | 26.4% CAGR |
There is increasing demand for deep learning in fraud detection, database systems, and cyber security is driving the growth of data mining applications in deep learning market. Healthcare industries produce a huge volume of data sets related to patient details, diagnosis, etc. Thus, data mining is anticipated to witness highest growth rate in the healthcare sector in the near future.
Furthermore, the proliferation of deep learning integration with big data analytics and rising need to improve computing power and decline hardware cost owing to deep learning algorithms capability to run or execute faster on a GPU as compared to a CPU is resulting in high adoption of deep learning technologies among various industries has positively anticipated in propelling the growth of global Deep Learning Market.
On the contrary, factors such as lack of technical expertise in deep learning, and the absence of standards and protocols are expected to hamper the growth of the Deep Learning Market in the forthcoming years.
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Rising necessity for hardware platforms with high computing power to execute deep learning algorithms is one of the major factors that is expected to accelerate the growth of the Deep Learning Market during the forecast period. In addition, the growing usage of deep learning in data analytics and database systems are expected to create lucrative growth opportunities for Deep Learning Market.
The technology is gaining prominence because of advancements in data center capabilities, high computing power, and the ability to perform tasks without human interactions. Moreover, the rapid adoption of cloud-based technology across several industries is fueling the growth of the market. Deep learning algorithms can perform several repetitive and routine tasks more efficiently within a shorter time than human beings. In addition, the quality of the work is maintained and provides accurate insights.
Thus, implementing deep learning in the organization can save time and money, which eventually frees up the employees to perform creative tasks that need human involvement. Therefore, deep learning is considered a disruptive technology across several end-use industries, uplifting the demand for technology.
The increasing adoption of cloud-based services and large-scale generation of unstructured data has surged the demand for deep learning solutions. Also, the growing applications of deep learning in recent years for image/speech recognition, data mining, and language translations, and the growing number of humanoid robots, for example, Sophia, developed by Hanson Robotics, are some of the major factors thar are anticipated to fuel the growth of the Deep Learning Market during the forecast period.
Growing investments for developing machine learning and deep learning applications in the region by key market players are expected to accelerate market growth. Moreover, the rapid increase in the amount of data being generated in different end-use industries is expected to provide traction to the industry growth. Additionally, the increasing need for human and machine interaction is offering new growth avenues to solution providers for providing enhanced solutions and capabilities.
Moreover, the advancements in technologies, the presence of limited structured data to increase demand for deep learning solutions, cumulative spending in healthcare, travel, tourism, and hospitality industries, and untapped potential in emerging markets are anticipated to offer favorable growth opportunities to the market growth.
There are certain restraints and challenges faced which are expected to impede the growth of the Deep Learning Market growth. The factors such as lack of technical expertise in deep learning and the absence of standards and protocols are limiting the market growth.
In addition, complex integrated systems and the integration of deep learning solutions and software into the existing systems is a difficult task which confines growth. Besides, increasing complexity in hardware due to complex algorithms, lack of flexibility and multitasking, and deployment of Deep Learning for applications such as NLP in regional dialects are the potential restraints that are hindering the overall growth of the global Deep Learning Market.
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In terms of regional platform, North America holds the largest market share in Deep Learning market. The region is expected to accumulate a 36.7% revenue share in 2022. The widespread adoption of deep learning technology is one of the major factors that is expected to propel the regional growth during the forecast period. There is high growth of deep learning market in the region, due to the presence of prominent players in the region that offers deep learning services and hardware such as IBM Corporation, Qualcomm Technologies, Inc. and Intel Corporation, etc.
Increased investments in artificial intelligence and neural networks, the high adoption of image and pattern recognition in the region are the major factors that are anticipated to open new growth avenues for the Deep Learning Market over the analysis period. Moreover, the region is one of the early adopters of advanced technologies, rendering organizations adopt deep learning capabilities at a faster pace.
According to Future Market Insights, Europe is expected to hold significant growth opportunities for Deep Learning Market, and is expected to reach at a share of 24.5% in 2022.
Increased government support is expected to provide a positive impact on the growth of the industry in the region. The establishment of subcommittees on artificial intelligence and machine learning within the federal government is providing traction for the growth.
Europe has contributed significantly to the market growth as several new measures have been taken to support the artificial intelligence sector in the region to boost growth and deliver a digital economy. This, in turn, has offered considerable growth opportunities in the deep learning space. The U.K. is underpinning the technology to grow further in the areas of autonomous vehicles, smart devices, and cyber security.
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According to Future Market Insights, Asia-Pacific is expected to grow with lucrative growth opportunities for Deep Learning Market, and is expected to reach at a significant share of 21.8% in 2022.
Deep learning is becoming more popular as this technology is employed in more than only electrical items like smartphones, tablets, and PCs, and yet also healthcare and automotive products. The rapid economic development of key nations such as China and India play a major role in encouraging the growth of the Asia Pacific Deep Learning Market.
Furthermore, rising government support is likely to have a positive impact on the region's industry growth. The growing need for the deep learning software, like signal recognition, image recognition, and data mining, in industries like healthcare, automotive, IT and telecommunications, aerospace & defence, as well as ongoing projects and the formation of artificial intelligence and machine learning subcommittees within the federal government, are the major factors that are projected to escalate the growth of the Deep Learning Market in Asia Pacific.
According to Future Market Insights, Middle East & Africa is expected to provide significant growth opportunities for Deep Learning Market, and is expected to reach at a share of 13.0% in 2022.
Middle East & Africa is expected to represent a significant share in the Deep Learning Market, owing to growing funding in artificial intelligence and neural networks. The region's widespread use of image and monitoring purposes is estimated to generate new growth prospects over the forecast period.
There are many prominent market players in the Deep Learning Market such as Augury, Alation, Algorithmia, Avora, Boast.ai, ClosedLoop.ai, Cognino AI, Databand, DataVisor, Exceed.ai, Indico, JAXJOX, LeadGenius, Netra, Particle, Resurface Labs, RideVision, Savvie, SECURITI.ai, SkyHive, Stravito, Uniphore, and Vertia.ai, among others, that are working hand-in-hand to provide the best-in-class Deep Learning for enhancing the global arena. However, there are many global start-ups in the Deep Learning Market, which are stepping forward in matching the requirements of the Deep Learning domain.
Some of the key participants present in the global Deep Learning market include NVIDIA Corporation, Intel Corporation, General Vision, Graphcore, General Vision Inc., Xilinx, Qualcomm Technologies, Google Inc., Microsoft Corporation, AWS, Sensory Inc., IBM Corporation, Samsung Electronics, Micron Technology, and Mellanox Technologies, among others.
Attributed to the presence of such high number of participants, the market is highly competitive. While global players such as NVIDIA Corporation, Intel Corporation, General Vision, Graphcore, General Vision Inc., and Xilinx, account for a considerable market size, several regional level players are also operating across key growth regions, particularly in North America.
| Report Attribute | Details |
|---|---|
| Growth Rate | CAGR of 26.4% from 2022 to 2032 |
| Base Year for Estimation | 2021 |
| Market Value in 2022 | US$ 12,569.0 Million |
| Market Value in 2032 | US$ 130,667.0 Million |
| Historical Data | 2016 to 2021 |
| Forecast Period | 2022 to 2032 |
| Quantitative Units | Revenue in USD Million and CAGR from 2022-2032 |
| Report Coverage | Revenue Forecast, Volume Forecast, Company Ranking, Competitive Landscape, Growth Factors, Trends and Pricing Analysis |
| Segments Covered |
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| Regions Covered |
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| Key Countries Profiled |
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| Key Companies Profiled |
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| Customization | Available Upon Request |
FMI projects the global deep learning market to expand at a 26.4% value CAGR by 2032
North America is expected to be the most opportunistic Deep Learning market, expanding at a 36.7% market share
NVIDIA Corporation, Intel Corporation, General Vision, Graphcore, General Vision Inc., and Xilinx, among others are some prominent Deep Learning players
1. Executive Summary 1.1. Global Market Outlook 1.2. Demand-side Trends 1.3. Supply-side Trends 1.4. Technology Roadmap Analysis 1.5. Analysis and Recommendations 2. Market Overview 2.1. Market Coverage / Taxonomy 2.2. Market Definition / Scope / Limitations 3. Market Background 3.1. Market Dynamics 3.1.1. Drivers 3.1.2. Restraints 3.1.3. Opportunity 3.1.4. Trends 3.2. Scenario Forecast 3.2.1. Demand in Optimistic Scenario 3.2.2. Demand in Likely Scenario 3.2.3. Demand in Conservative Scenario 3.3. Opportunity Map Analysis 3.4. Investment Feasibility Matrix 3.5. PESTLE and Porter’s Analysis 3.6. Regulatory Landscape 3.6.1. By Key Regions 3.6.2. By Key Countries 3.7. Regional Parent Market Outlook 4. Global Market Analysis 2016-2021 and Forecast, 2022-2032 4.1. Historical Market Size Value (US$ Mn) Analysis, 2016-2021 4.2. Current and Future Market Size Value (US$ Mn) Projections, 2022-2032 4.2.1. Y-o-Y Growth Trend Analysis 4.2.2. Absolute $ Opportunity Analysis 5. Global Market Analysis 2016-2021 and Forecast 2022-2032, By Product 5.1. Introduction / Key Findings 5.2. Historical Market Size Value (US$ Mn) Analysis By Product, 2016-2021 5.3. Current and Future Market Size Value (US$ Mn) Analysis and Forecast By Product, 2022-2032 5.3.1. Hardware 5.3.2. Software 5.3.3. Services 5.4. Y-o-Y Growth Trend Analysis By Product, 2016-2021 5.5. Absolute $ Opportunity Analysis By Product, 2022-2032 6. Global Market Analysis 2016-2021 and Forecast 2022-2032, By Application 6.1. Introduction / Key Findings 6.2. Historical Market Size Value (US$ Mn) Analysis By Application, 2016-2021 6.3. Current and Future Market Size Value (US$ Mn) Analysis and Forecast By Application, 2022-2032 6.3.1. Image Recognition 6.3.2. Signal Recognition 6.3.3. Translation 6.3.4. Data Mining 6.4. Y-o-Y Growth Trend Analysis By Application, 2016-2021 6.5. Absolute $ Opportunity Analysis By Application, 2022-2032 7. Global Market Analysis 2016-2021 and Forecast 2022-2032, By End Users 7.1. Introduction / Key Findings 7.2. Historical Market Size Value (US$ Mn) Analysis By End Users, 2016-2021 7.3. Current and Future Market Size Value (US$ Mn) Analysis and Forecast By End Users, 2022-2032 7.3.1. Automotive 7.3.2. Media & Entertainment 7.3.3. Aerospace & Defence 7.3.4. BFSI 7.3.5. Retail 7.3.6. Healthcare 7.4. Y-o-Y Growth Trend Analysis By End Users, 2016-2021 7.5. Absolute $ Opportunity Analysis By End Users, 2022-2032 8. Global Market Analysis 2016-2021 and Forecast 2022-2032, By Region 8.1. Introduction 8.2. Historical Market Size Value (US$ Mn) Analysis By Region, 2016-2021 8.3. Current Market Size Value (US$ Mn) Analysis and Forecast By Region, 2022-2032 8.3.1. North America 8.3.2. Latin America 8.3.3. Europe 8.3.4. Asia Pacific 8.3.5. MEA 8.4. Market Attractiveness Analysis By Region 9. North America Market Analysis 2016-2021 and Forecast 2022-2032, By Country 9.1. Historical Market Size Value (US$ Mn) Trend Analysis By Market Taxonomy, 2016-2021 9.2. Market Size Value (US$ Mn) Forecast By Market Taxonomy, 2022-2032 9.2.1. By Country 9.2.1.1. U.S. 9.2.1.2. Canada 9.2.2. By Product 9.2.3. By Application 9.2.4. By End Users 9.3. Market Attractiveness Analysis 9.3.1. By Country 9.3.2. By Product 9.3.3. By Application 9.3.4. By End Users 9.4. Key Takeaways 10. Latin America Market Analysis 2016-2021 and Forecast 2022-2032, By Country 10.1. Historical Market Size Value (US$ Mn) Trend Analysis By Market Taxonomy, 2016-2021 10.2. Market Size Value (US$ Mn) Forecast By Market Taxonomy, 2022-2032 10.2.1. By Country 10.2.1.1. Brazil 10.2.1.2. Mexico 10.2.1.3. Rest of Latin America 10.2.2. By Product 10.2.3. By Application 10.2.4. By End Users 10.3. Market Attractiveness Analysis 10.3.1. By Country 10.3.2. By Product 10.3.3. By Application 10.3.4. By End Users 10.4. Key Takeaways 11. Europe Market Analysis 2016-2021 and Forecast 2022-2032, By Country 11.1. Historical Market Size Value (US$ Mn) Trend Analysis By Market Taxonomy, 2016-2021 11.2. Market Size Value (US$ Mn) Forecast By Market Taxonomy, 2022-2032 11.2.1. By Country 11.2.1.1. Germany 11.2.1.2. U.K. 11.2.1.3. France 11.2.1.4. Spain 11.2.1.5. Italy 11.2.1.6. Rest of Europe 11.2.2. By Product 11.2.3. By Application 11.2.4. By End Users 11.3. Market Attractiveness Analysis 11.3.1. By Country 11.3.2. By Product 11.3.3. By Application 11.3.4. By End Users 11.4. Key Takeaways 12. Asia Pacific Market Analysis 2016-2021 and Forecast 2022-2032, By Country 12.1. Historical Market Size Value (US$ Mn) Trend Analysis By Market Taxonomy, 2016-2021 12.2. Market Size Value (US$ Mn) Forecast By Market Taxonomy, 2022-2032 12.2.1. By Country 12.2.1.1. China 12.2.1.2. Japan 12.2.1.3. South Korea 12.2.1.4. Malaysia 12.2.1.5. Singapore 12.2.1.6. Australia 12.2.1.7. Rest of Asia Pacific 12.2.2. By Product 12.2.3. By Application 12.2.4. By End Users 12.3. Market Attractiveness Analysis 12.3.1. By Country 12.3.2. By Product 12.3.3. By Application 12.3.4. By End Users 12.4. Key Takeaways 13. MEA Market Analysis 2016-2021 and Forecast 2022-2032, By Country 13.1. Historical Market Size Value (US$ Mn) Trend Analysis By Market Taxonomy, 2016-2021 13.2. Market Size Value (US$ Mn) Forecast By Market Taxonomy, 2022-2032 13.2.1. By Country 13.2.1.1. GCC Countries 13.2.1.2. South Africa 13.2.1.3. Israel 13.2.1.4. Rest of MEA 13.2.2. By Product 13.2.3. By Application 13.2.4. By End Users 13.3. Market Attractiveness Analysis 13.3.1. By Country 13.3.2. By Product 13.3.3. By Application 13.3.4. By End Users 13.4. Key Takeaways 14. Key Countries Market Analysis 14.1. U.K. 14.1.1. Pricing Analysis 14.1.2. Market Share Analysis, 2021 14.1.2.1. By Product 14.1.2.2. By Application 14.1.2.3. By End Users 14.2. Canada 14.2.1. Pricing Analysis 14.2.2. Market Share Analysis, 2021 14.2.2.1. By Product 14.2.2.2. By Application 14.2.2.3. By End Users 14.3. Brazil 14.3.1. Pricing Analysis 14.3.2. Market Share Analysis, 2021 14.3.2.1. By Product 14.3.2.2. By Application 14.3.2.3. By End Users 14.4. Mexico 14.4.1. Pricing Analysis 14.4.2. Market Share Analysis, 2021 14.4.2.1. By Product 14.4.2.2. By Application 14.4.2.3. By End Users 14.5. Rest of Latin America 14.5.1. Pricing Analysis 14.5.2. Market Share Analysis, 2021 14.5.2.1. By Product 14.5.2.2. By Application 14.5.2.3. By End Users 14.6. Germany 14.6.1. Pricing Analysis 14.6.2. Market Share Analysis, 2021 14.6.2.1. By Product 14.6.2.2. By Application 14.6.2.3. By End Users 14.7. U.K. 14.7.1. Pricing Analysis 14.7.2. Market Share Analysis, 2021 14.7.2.1. By Product 14.7.2.2. By Application 14.7.2.3. By End Users 14.8. France 14.8.1. Pricing Analysis 14.8.2. Market Share Analysis, 2021 14.8.2.1. By Product 14.8.2.2. By Application 14.8.2.3. By End Users 14.9. Spain 14.9.1. Pricing Analysis 14.9.2. Market Share Analysis, 2021 14.9.2.1. By Product 14.9.2.2. By Application 14.9.2.3. By End Users 14.10. Italy 14.10.1. Pricing Analysis 14.10.2. Market Share Analysis, 2021 14.10.2.1. By Product 14.10.2.2. By Application 14.10.2.3. By End Users 14.11. Rest of Europe 14.11.1. Pricing Analysis 14.11.2. Market Share Analysis, 2021 14.11.2.1. By Product 14.11.2.2. By Application 14.11.2.3. By End Users 14.12. China 14.12.1. Pricing Analysis 14.12.2. Market Share Analysis, 2021 14.12.2.1. By Product 14.12.2.2. By Application 14.12.2.3. By End Users 14.13. Japan 14.13.1. Pricing Analysis 14.13.2. Market Share Analysis, 2021 14.13.2.1. By Product 14.13.2.2. By Application 14.13.2.3. By End Users 14.14. South Korea 14.14.1. Pricing Analysis 14.14.2. Market Share Analysis, 2021 14.14.2.1. By Product 14.14.2.2. By Application 14.14.2.3. By End Users 14.15. Malaysia 14.15.1. Pricing Analysis 14.15.2. Market Share Analysis, 2021 14.15.2.1. By Product 14.15.2.2. By Application 14.15.2.3. By End Users 14.16. Singapore 14.16.1. Pricing Analysis 14.16.2. Market Share Analysis, 2021 14.16.2.1. By Product 14.16.2.2. By Application 14.16.2.3. By End Users 14.17. Australia 14.17.1. Pricing Analysis 14.17.2. Market Share Analysis, 2021 14.17.2.1. By Product 14.17.2.2. By Application 14.17.2.3. By End Users 14.18. Rest of Asia Pacific 14.18.1. Pricing Analysis 14.18.2. Market Share Analysis, 2021 14.18.2.1. By Product 14.18.2.2. By Application 14.18.2.3. By End Users 14.19. GCC Countries 14.19.1. Pricing Analysis 14.19.2. Market Share Analysis, 2021 14.19.2.1. By Product 14.19.2.2. By Application 14.19.2.3. By End Users 14.20. South Africa 14.20.1. Pricing Analysis 14.20.2. Market Share Analysis, 2021 14.20.2.1. By Product 14.20.2.2. By Application 14.20.2.3. By End Users 14.21. Israel 14.21.1. Pricing Analysis 14.21.2. Market Share Analysis, 2021 14.21.2.1. By Product 14.21.2.2. By Application 14.21.2.3. By End Users 14.22. Rest of MEA 14.22.1. Pricing Analysis 14.22.2. Market Share Analysis, 2021 14.22.2.1. By Product 14.22.2.2. By Application 14.22.2.3. By End Users 15. Market Structure Analysis 15.1. Competition Dashboard 15.2. Competition Benchmarking 15.3. Market Share Analysis of Top Players 15.3.1. By Regional 15.3.2. By Product 15.3.3. By Application 15.3.4. By End Users 16. Competition Analysis 16.1. Competition Deep Dive 16.1.1. Google Inc. 16.1.1.1. Overview 16.1.1.2. Product Portfolio 16.1.1.3. Profitability by Market Segments 16.1.1.4. Sales Footprint 16.1.1.5. Strategy Overview 16.1.1.5.1. Marketing Strategy 16.1.2. Microsoft Corporation 16.1.2.1. Overview 16.1.2.2. Product Portfolio 16.1.2.3. Profitability by Market Segments 16.1.2.4. Sales Footprint 16.1.2.5. Strategy Overview 16.1.2.5.1. Marketing Strategy 16.1.3. Qualcomm Technologies 16.1.3.1. Overview 16.1.3.2. Product Portfolio 16.1.3.3. Profitability by Market Segments 16.1.3.4. Sales Footprint 16.1.3.5. Strategy Overview 16.1.3.5.1. Marketing Strategy 16.1.4. IBM Corporation 16.1.4.1. Overview 16.1.4.2. Product Portfolio 16.1.4.3. Profitability by Market Segments 16.1.4.4. Sales Footprint 16.1.4.5. Strategy Overview 16.1.4.5.1. Marketing Strategy 16.1.5. Intel Corporation 16.1.5.1. Overview 16.1.5.2. Product Portfolio 16.1.5.3. Profitability by Market Segments 16.1.5.4. Sales Footprint 16.1.5.5. Strategy Overview 16.1.5.5.1. Marketing Strategy 16.1.6. General Vision Inc. 16.1.6.1. Overview 16.1.6.2. Product Portfolio 16.1.6.3. Profitability by Market Segments 16.1.6.4. Sales Footprint 16.1.6.5. Strategy Overview 16.1.6.5.1. Marketing Strategy 16.1.7. NVIDIA Corporation 16.1.7.1. Overview 16.1.7.2. Product Portfolio 16.1.7.3. Profitability by Market Segments 16.1.7.4. Sales Footprint 16.1.7.5. Strategy Overview 16.1.7.5.1. Marketing Strategy 16.1.8. Facebook Inc. 16.1.8.1. Overview 16.1.8.2. Product Portfolio 16.1.8.3. Profitability by Market Segments 16.1.8.4. Sales Footprint 16.1.8.5. Strategy Overview 16.1.8.5.1. Marketing Strategy 16.1.9. Amazon Web Services Inc. 16.1.9.1. Overview 16.1.9.2. Product Portfolio 16.1.9.3. Profitability by Market Segments 16.1.9.4. Sales Footprint 16.1.9.5. Strategy Overview 16.1.9.5.1. Marketing Strategy 16.1.10. SAS Institute Inc. 16.1.10.1. Overview 16.1.10.2. Product Portfolio 16.1.10.3. Profitability by Market Segments 16.1.10.4. Sales Footprint 16.1.10.5. Strategy Overview 16.1.10.5.1. Marketing Strategy 17. Assumptions & Acronyms Used 18. Research Methodology
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Deep Learning Market
