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Deep Learning Market Snapshot (2022-2032)

[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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Which are Some Prominent Drivers Spearheading Deep Learning Market Growth?

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.

What are the Challenges Faced by the Deep Learning Industry?

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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Why is North America Emerging as an Opportunistic Deep Learning Market?

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.

How is Europe Contributing to Growth of the Deep Learning Market?

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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Where does Asia-Pacific region stand in the Deep Learning Market?

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.

How is Middle East & Africa Contributing to Growth of the Deep Learning Market?

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.

Role of Start-ups in Deep Learning Market

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.

  • Established in 2012, Algorithmia is a pioneer in Deep Learning, whose has an expertise in machine learning operations (MLOps). The company is helping customers deliver ML models to production with enterprise-grade security and governance. Algorithmia automates ML deployment, provides tooling flexibility, enables collaboration between operations and development and leverages existing SDLC and CI/CD practices. Over 110,000 engineers and data scientists have used Algorithmia’s platform to date, including the United Nations, government intelligence agencies and Fortune 500 companies.
  • Founded in 2011, Augury combines real-time monitoring data from production machinery with AI and machine learning algorithms to determine machine health, asset performance management (APM) and predictive maintenance (PdM) to provide manufacturing companies with new insights into their operations. The digital machine health technology that the company offers can listen to the machine, analyze the data and catch any malfunctions before they arise. This enables customers to adjust their maintenance and manufacturing processes based on actual machine conditions. The platform is in use with HVAC, industrial factories and commercial facilities.
  • Founded in 2012, Alation is credited with pioneering the data catalog market and is well-respected in the financial services community for its use of A.I. to interpret and present data for analysis. Alation has also set a quick pace to evolving its platform to include data search & discovery, data governance, data stewardship, analytics and digital transformation. With its Behavioral Analysis Engine, inbuilt collaboration capabilities and open interfaces, Alation combines machine learning with human insight to successfully tackle data and metadata management challenges. More than 200 enterprises are using Alation’s platform today, including AbbVie, American Family Insurance, Cisco, Exelon, Finnair, Munich Re, New Balance, Pfizer, Scandinavian Airlines and U.S. Foods. Headquartered in Silicon Valley, Alation is backed by leading venture capitalists including Costanoa, Data Collective, Icon, Sapphire and Salesforce Ventures.

Market Competition

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.

Recent Developments

  • In June 2021, Larsen & Toubro Infotech entered into a strategic collaboration agreement with Amazon Web Services. The company recently launched a dedicated cloud unit for AWS, which will focus on migration and modernization, SAP application workloads, data analytics, and the Internet of things. It will also provide advisory, professional services, and delivery capabilities.
  • In July 2020, Tencent AI Lab and a group of Chinese public health scientists unveiled a deep learning-based model that could predict the risk of COVID-19 patients developing the critical illness. The procedure was published in Nature Communications. It revised the lab’s method based on a cohort of 1,590 patients from 575 medical centers in China, with further validation from 1,393 patients.
  • In May 2020, NEUCHIPS Corp., an Artificial Intelligence computing company engaging in domain-specific accelerator solutions, launched the world’s first deep learning recommendation engine – RecAccelTM – that can perform 500,000 inferences per second. Running open-source PyTorch DLRM, RecAccelTM outperforms inference GPU and server-class CPU by 65X and 28X, respectively.

Report Scope

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
  • Product
  • Application
  • End User
  • Region
Regions Covered
  • North America
  • Latin America
  • Europe
  • Asia Pacific
  • Middle East and Africa
Key Countries Profiled
  • USA
  • Canada
  • Brazil
  • Mexico
  • Germany
  • UK
  • France
  • Spain
  • Italy
  • China
  • Japan
  • South Korea
  • Malaysia
  • Singapore
  • Australia
  • GCC
  • South Africa
  • Israel
Key Companies Profiled
  • NVIDIA Corporation
  • Intel Corporation
  • General Vision Inc.
  • Graphcore
  • Xilinx
  • Qualcomm Technologies
  • Google Inc.
  • Microsoft Corporation
  • Amazon Web Services
  • Sensory Inc.
  • IBM Corporation
  • Samsung Electronics
  • Micron Technology
  • Mellanox Technologies
  • Facebook Inc.
  • SAS Institute Inc.
Customization Available Upon Request

Key Segments Profiled in the Deep Learning Industry Survey

Deep Learning Market by Product:

  • Hardware
  • Software
  • Services

Deep Learning Market by Application:

  • Image Recognition
  • Signal Recognition
  • Translation
  • Data Mining

Deep Learning Market by End User:

  • Automotive
  • Media & Entertainment
  • Aerospace & Defence
  • BFSI
  • Retail
  • Healthcare

Deep Learning Market By Region:

  • North America Deep Learning Market
  • Europe Deep Learning Market
  • Latin America Deep Learning Market
  • Asia Pacific Deep Learning Market
  • Middle East & Africa Deep Learning Market

Frequently Asked Questions

What is the anticipated growth of the Deep Learning market until 2032?

FMI projects the global deep learning market to expand at a 26.4% value CAGR by 2032

Which region is forecast to be the most lucrative for Deep Learning market growth?

North America is expected to be the most opportunistic Deep Learning market, expanding at a 36.7% market share

Which are some prominent Deep Learning players?

NVIDIA Corporation, Intel Corporation, General Vision, Graphcore, General Vision Inc., and Xilinx, among others are some prominent Deep Learning players

Table of Content

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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