세계의 딥러닝 시장 : 유형별, 최종 사용자별, 용도별 예측(2025-2030년)

The Deep Learning Market was valued at USD 5.57 billion in 2023, expected to reach USD 7.24 billion in 2024, and is projected to grow at a CAGR of 30.39%, to USD 35.71 billion by 2030.

Deep learning, a subset of machine learning in the field of artificial intelligence (AI), is designed to simulate human brain function by learning from vast amounts of data. Its scope encompasses diverse sectors including consumer electronics, healthcare, automotive, finance, and retail, underlining its necessity due to its capacity to enhance tasks like image and speech recognition, natural language processing, and complex problem-solving. The end-use scope of deep learning is vast; from chatbots enhancing customer service in retail to autonomous driving technologies in automotive, and diagnostic tools in healthcare, its applications fundamentally transform services and operational efficiencies across industries. Key growth factors influencing the deep learning market include exponential growth in data generation, advances in computing power, and the surge in AI-driven applications across numerous sectors. These elements collectively drive substantial investment, fueling rapid market expansion. The latest potential opportunities lie in sectors like healthcare, where deep learning can lead to breakthroughs in personalized medicine and predictive analytics, and financial services for fraud detection and algorithmic trading. Recommendations to leverage these opportunities include focusing on innovation in edge computing and AI-powered cybersecurity, where demand is skyrocketing. Nonetheless, market growth faces limitations including high implementation costs, data privacy concerns, and a skills gap in AI expertise. Addressing these involves dedicating resources to training and development alongside fostering partnerships with educational institutions. Challenging factors also include regulatory challenges and ethical considerations surrounding AI deployment. Innovative areas for business growth lie in democratizing AI capabilities, making them accessible for small and mid-sized businesses, and developing AI models that offer transparency and explainability. Overall, the market is dynamic and competitive, characterized by rapid technological evolution and the need for companies to remain agile and responsive to emerging trends and regulatory landscapes.

Market Dynamics: Unveiling Key Market Insights in the Rapidly Evolving Deep Learning Market

The Deep Learning Market is undergoing transformative changes driven by a dynamic interplay of supply and demand factors. Understanding these evolving market dynamics prepares business organizations to make informed investment decisions, refine strategic decisions, and seize new opportunities. By gaining a comprehensive view of these trends, business organizations can mitigate various risks across political, geographic, technical, social, and economic domains while also gaining a clearer understanding of consumer behavior and its impact on manufacturing costs and purchasing trends.

• Market Drivers
  • Increasing adoption of cloud-based technology
  • Growing AI adoption in customer centric services
  • Rising applications in healthcare, manufacturing, and automotive industries
• Market Restraints
  • Lack of flexibility and multitasking
• Market Opportunities
  • Rapid introduction of self-driving technology
  • Recent developments in neural network architecture and training algorithms
• Market Challenges
  • Lack of technical expertise and absence of standards and protocols

Porter's Five Forces: A Strategic Tool for Navigating the Deep Learning Market

Porter's five forces framework is a critical tool for understanding the competitive landscape of the Deep Learning Market. It offers business organizations with a clear methodology for evaluating their competitive positioning and exploring strategic opportunities. This framework helps businesses assess the power dynamics within the market and determine the profitability of new ventures. With these insights, business organizations can leverage their strengths, address weaknesses, and avoid potential challenges, ensuring a more resilient market positioning.

PESTLE Analysis: Navigating External Influences in the Deep Learning Market

External macro-environmental factors play a pivotal role in shaping the performance dynamics of the Deep Learning Market. Political, Economic, Social, Technological, Legal, and Environmental factors analysis provides the necessary information to navigate these influences. By examining PESTLE factors, businesses can better understand potential risks and opportunities. This analysis enables business organizations to anticipate changes in regulations, consumer preferences, and economic trends, ensuring they are prepared to make proactive, forward-thinking decisions.

Market Share Analysis: Understanding the Competitive Landscape in the Deep Learning Market

A detailed market share analysis in the Deep Learning Market provides a comprehensive assessment of vendors' performance. Companies can identify their competitive positioning by comparing key metrics, including revenue, customer base, and growth rates. This analysis highlights market concentration, fragmentation, and trends in consolidation, offering vendors the insights required to make strategic decisions that enhance their position in an increasingly competitive landscape.

FPNV Positioning Matrix: Evaluating Vendors' Performance in the Deep Learning Market

The Forefront, Pathfinder, Niche, Vital (FPNV) Positioning Matrix is a critical tool for evaluating vendors within the Deep Learning Market. This matrix enables business organizations to make well-informed decisions that align with their goals by assessing vendors based on their business strategy and product satisfaction. The four quadrants provide a clear and precise segmentation of vendors, helping users identify the right partners and solutions that best fit their strategic objectives.

Strategy Analysis & Recommendation: Charting a Path to Success in the Deep Learning Market

A strategic analysis of the Deep Learning Market is essential for businesses looking to strengthen their global market presence. By reviewing key resources, capabilities, and performance indicators, business organizations can identify growth opportunities and work toward improvement. This approach helps businesses navigate challenges in the competitive landscape and ensures they are well-positioned to capitalize on newer opportunities and drive long-term success.

Key Company Profiles

The report delves into recent significant developments in the Deep Learning Market, highlighting leading vendors and their innovative profiles. These include Advanced Micro Devices, Inc., ARM Ltd., Broadcom Corporation, CEVA Inc., Clarifai, Inc., Google LLC, Huawei Technologies, Intel Corporation, International Business Machines Corporation, Microsoft Corporation, Neurala, NVIDIA Corporation, OpenAI, Qualcomm Technologies, Inc, Samsung Group, and Starmind.

Market Segmentation & Coverage

This research report categorizes the Deep Learning Market to forecast the revenues and analyze trends in each of the following sub-markets:

• Based on Type, market is studied across Hardware, Services, and Software. The Hardware is further studied across Central Processing Unit, Field Programmable Gate Array, and Graphics Processing Unit. The Software is further studied across Platform or API and Solutions.
• Based on End-User, market is studied across Agriculture, Automotive, Fintech, Healthcare, Human Resources, Law, Manufacturing, Marketing, Retail, and Security.
• Based on Application, market is studied across Data Mining, Image Recognition, and Signal Recognition.
• Based on Region, market is studied across Americas, Asia-Pacific, and Europe, Middle East & Africa. The Americas is further studied across Argentina, Brazil, Canada, Mexico, and United States. The United States is further studied across California, Florida, Illinois, New York, Ohio, Pennsylvania, and Texas. The Asia-Pacific is further studied across Australia, China, India, Indonesia, Japan, Malaysia, Philippines, Singapore, South Korea, Taiwan, Thailand, and Vietnam. The Europe, Middle East & Africa is further studied across Denmark, Egypt, Finland, France, Germany, Israel, Italy, Netherlands, Nigeria, Norway, Poland, Qatar, Russia, Saudi Arabia, South Africa, Spain, Sweden, Switzerland, Turkey, United Arab Emirates, and United Kingdom.

The report offers a comprehensive analysis of the market, covering key focus areas:

1. Market Penetration: A detailed review of the current market environment, including extensive data from top industry players, evaluating their market reach and overall influence.

2. Market Development: Identifies growth opportunities in emerging markets and assesses expansion potential in established sectors, providing a strategic roadmap for future growth.

3. Market Diversification: Analyzes recent product launches, untapped geographic regions, major industry advancements, and strategic investments reshaping the market.

4. Competitive Assessment & Intelligence: Provides a thorough analysis of the competitive landscape, examining market share, business strategies, product portfolios, certifications, regulatory approvals, patent trends, and technological advancements of key players.

5. Product Development & Innovation: Highlights cutting-edge technologies, R&D activities, and product innovations expected to drive future market growth.

The report also answers critical questions to aid stakeholders in making informed decisions:

1. What is the current market size, and what is the forecasted growth?

2. Which products, segments, and regions offer the best investment opportunities?

3. What are the key technology trends and regulatory influences shaping the market?

4. How do leading vendors rank in terms of market share and competitive positioning?

5. What revenue sources and strategic opportunities drive vendors' market entry or exit strategies?

Table of Contents

1. Preface

• 1.1. Objectives of the Study
• 1.2. Market Segmentation & Coverage
• 1.3. Years Considered for the Study
• 1.4. Currency & Pricing
• 1.5. Language
• 1.6. Stakeholders

2. Research Methodology

• 2.1. Define: Research Objective
• 2.2. Determine: Research Design
• 2.3. Prepare: Research Instrument
• 2.4. Collect: Data Source
• 2.5. Analyze: Data Interpretation
• 2.6. Formulate: Data Verification
• 2.7. Publish: Research Report
• 2.8. Repeat: Report Update

3. Executive Summary

4. Market Overview

5. Market Insights

• 5.1. Market Dynamics
  • 5.1.1. Drivers
    • 5.1.1.1. Increasing adoption of cloud-based technology
    • 5.1.1.2. Growing AI adoption in customer centric services
    • 5.1.1.3. Rising applications in healthcare, manufacturing, and automotive industries
  • 5.1.2. Restraints
    • 5.1.2.1. Lack of flexibility and multitasking
  • 5.1.3. Opportunities
    • 5.1.3.1. Rapid introduction of self-driving technology
    • 5.1.3.2. Recent developments in neural network architecture and training algorithms
  • 5.1.4. Challenges
    • 5.1.4.1. Lack of technical expertise and absence of standards and protocols
• 5.2. Market Segmentation Analysis
• 5.3. Porter's Five Forces Analysis
  • 5.3.1. Threat of New Entrants
  • 5.3.2. Threat of Substitutes
  • 5.3.3. Bargaining Power of Customers
  • 5.3.4. Bargaining Power of Suppliers
  • 5.3.5. Industry Rivalry
• 5.4. PESTLE Analysis
  • 5.4.1. Political
  • 5.4.2. Economic
  • 5.4.3. Social
  • 5.4.4. Technological
  • 5.4.5. Legal
  • 5.4.6. Environmental

6. Deep Learning Market, by Type

• 6.1. Introduction
• 6.2. Hardware
  • 6.2.1. Central Processing Unit
  • 6.2.2. Field Programmable Gate Array
  • 6.2.3. Graphics Processing Unit
• 6.3. Services
• 6.4. Software
  • 6.4.1. Platform or API
  • 6.4.2. Solutions

7. Deep Learning Market, by End-User

• 7.1. Introduction
• 7.2. Agriculture
• 7.3. Automotive
• 7.4. Fintech
• 7.5. Healthcare
• 7.6. Human Resources
• 7.7. Law
• 7.8. Manufacturing
• 7.9. Marketing
• 7.10. Retail
• 7.11. Security

8. Deep Learning Market, by Application

• 8.1. Introduction
• 8.2. Data Mining
• 8.3. Image Recognition
• 8.4. Signal Recognition

9. Americas Deep Learning Market

• 9.1. Introduction
• 9.2. Argentina
• 9.3. Brazil
• 9.4. Canada
• 9.5. Mexico
• 9.6. United States

10. Asia-Pacific Deep Learning Market

• 10.1. Introduction
• 10.2. Australia
• 10.3. China
• 10.4. India
• 10.5. Indonesia
• 10.6. Japan
• 10.7. Malaysia
• 10.8. Philippines
• 10.9. Singapore
• 10.10. South Korea
• 10.11. Taiwan
• 10.12. Thailand
• 10.13. Vietnam

11. Europe, Middle East & Africa Deep Learning Market

• 11.1. Introduction
• 11.2. Denmark
• 11.3. Egypt
• 11.4. Finland
• 11.5. France
• 11.6. Germany
• 11.7. Israel
• 11.8. Italy
• 11.9. Netherlands
• 11.10. Nigeria
• 11.11. Norway
• 11.12. Poland
• 11.13. Qatar
• 11.14. Russia
• 11.15. Saudi Arabia
• 11.16. South Africa
• 11.17. Spain
• 11.18. Sweden
• 11.19. Switzerland
• 11.20. Turkey
• 11.21. United Arab Emirates
• 11.22. United Kingdom

12. Competitive Landscape

• 12.1. Market Share Analysis, 2023
• 12.2. FPNV Positioning Matrix, 2023
• 12.3. Competitive Scenario Analysis
• 12.4. Strategy Analysis & Recommendation

Companies Mentioned

• 1. Advanced Micro Devices, Inc.
• 2. ARM Ltd.
• 3. Broadcom Corporation
• 4. CEVA Inc.
• 5. Clarifai, Inc.
• 6. Google LLC
• 7. Huawei Technologies
• 8. Intel Corporation
• 9. International Business Machines Corporation
• 10. Microsoft Corporation
• 11. Neurala
• 12. NVIDIA Corporation
• 13. OpenAI
• 14. Qualcomm Technologies, Inc
• 15. Samsung Group
• 16. Starmind