深度學習市場機會、成長動力、產業趨勢分析以及 2024 年至 2032 年預測

2023年全球深度學習市場估值為198億美元，預計2024年至2032年複合年成長率為30.4%。公司正在尋求提高效率、降低成本並最大限度地減少人為錯誤，而深度學習技術為自動化複雜任務提供了有效的解決方案。雲端運算的興起進一步推動了深度學習市場的發展。雲端平台提供可擴展且靈活的資源，使企業無需大量初始硬體投資即可存取高效能運算。

這使得公司可以更輕鬆地實施深度學習解決方案、管理大型資料集、訓練複雜的模型以及快速部署應用程式。 AWS、Google Cloud 和 Microsoft Azure 等領先的雲端供應商提供專業的深度學習服務。這些平台提供了預先建置的框架和工具，可以簡化開發過程，推動創新並增加深度學習技術的採用。隨著越來越多的公司採用雲端運算進行資料處理，對深度學習解決方案的需求將持續成長。

市場根據組件分為硬體、軟體和服務。 2023 年，軟體領域佔據了超過 30% 的市場佔有率，預計到 2032 年將超過 800 億美元。這些工具使開發人員可以更輕鬆地建置、訓練和部署神經網路。從應用來看，深度學習市場分為影像辨識、語音辨識、訊號辨識、資料處理等。

到 2023 年，影像辨識領域約佔市場的 31%。例如，在醫療保健領域，它用於分析醫學影像，從而實現更早的疾病檢測和更好的病患照護。在人工智慧研發的強勁投資推動下，美國深度學習市場佔了 75% 的佔有率。

政府和私部門的資金都創造了有利於深度學習創新的環境。此外，歐洲的政府措施和有利的監管框架促進了人工智慧的發展，進一步推動了該地區的深度學習市場。許多歐洲國家都專注於推動人工智慧技術，同時確保維持道德標準。

目錄

第 1 章：方法與範圍

第 2 章：執行摘要

第 3 章：產業洞察

• 產業生態系統分析
  • 硬體提供者
  • 軟體供應商
  • 服務提供者
  • 技術提供者
  • 終端用戶
• 供應商格局
• 利潤率分析
• 深度學習架構
• 案例研究
• 技術與創新格局
• 重要新聞和舉措
• 監管環境
• 衝擊力
  • 成長動力
    • 深度學習技術的快速進步
    • 對人工智慧驅動的解決方案的需求不斷成長
    • 加大政府支持與舉措
    • 深度學習投資不斷增加
  • 產業陷阱與挑戰
    • 資料隱私問題
    • 計算成本高
• 成長潛力分析
• 波特的分析
• PESTEL分析

第 4 章：競爭格局

• 介紹
• 公司市佔率分析
• 競爭定位矩陣
• 戰略展望矩陣

第 5 章：市場估計與預測：按組成部分，2021 - 2032 年

• 主要趨勢
• 硬體
  • GPU
  • FPGA
  • ASIC
  • TPU
  • 其他
• 軟體
• 服務
  • 專業的
  • 託管

第 6 章：市場估計與預測：依組織規模，2021 - 2032 年

• 主要趨勢
• 中小企業
• 大型組織

第 7 章：市場估計與預測：依應用分類，2021 - 2032

• 主要趨勢
• 語音辨識
• 影像辨識
• 訊號識別
• 資料處理
• 其他

第 8 章：市場估計與預測：依最終用途，2021 - 2032 年

• 主要趨勢
• BFSI
• 資訊科技與電信
• 汽車
• 衛生保健
• 零售與電子商務
• 製造業
• 媒體與娛樂
• 其他

第 9 章：市場估計與預測：按地區，2021 - 2032

• 主要趨勢
• 北美洲
  • 美國
  • 加拿大
• 歐洲
  • 英國
  • 德國
  • 法國
  • 西班牙
  • 義大利
  • 俄羅斯
  • 北歐人
• 亞太地區
  • 中國
  • 印度
  • 日本
  • 韓國
  • 澳新銀行
  • 東南亞
• 拉丁美洲
  • 巴西
  • 墨西哥
  • 阿根廷
• MEA
  • 阿拉伯聯合大公國
  • 南非
  • 沙烏地阿拉伯

第 10 章：公司簡介

• Adobe Inc.
• Advanced Micro Devices, Inc.
• Alibaba
• Amazon Web Services (AWS)
• Baidu, Inc.
• Google LLC
• Hewlett Packard Enterprise (HPE)
• IBM Corporation
• Intel Corporation
• Meta Platforms, Inc.
• Microsoft Corporation
• NVIDIA Corporation
• Oracle Corporation
• Qualcomm
• Salesforce
• SAP SE
• SenseTime
• Tencent Holdings Ltd.
• UiPath Inc.

The Global Deep Learning Market was valued at USD 19.8 billion in 2023 and is expected to grow at CAGR of 30.4% from 2024 to 2032. The increasing demand for automation across industries is a major factor driving this growth. Companies are looking to improve efficiency, reduce costs, and minimize human errors, and deep learning technologies provide effective solutions for automating complex tasks. The rise of cloud computing is further fueling the deep learning market. Cloud platforms offer scalable and flexible resources, allowing businesses to access high-performance computing without large initial hardware investments.

This makes it easier for companies to implement deep learning solutions, manage large datasets, train sophisticated models, and deploy applications quickly. Leading cloud providers, including AWS, Google Cloud, and Microsoft Azure, offer specialized deep learning services. These platforms provide pre-built frameworks and tools that simplify the development process, driving innovation and increasing adoption of deep learning technologies. As more companies embrace cloud computing for data processing, the demand for deep learning solutions will continue to grow.

The market is segmented into hardware, software, and services based on components. In 2023, the software segment captured over 30% of the market and is expected to surpass USD 80 billion by 2032. The growth in the software segment is driven by advancements in frameworks specifically designed for deep learning, such as TensorFlow, PyTorch, and Keras. These tools make it easier for developers to build, train, and deploy neural networks. In terms of applications, the deep learning market is categorized into image recognition, speech recognition, signal recognition, data processing, and others.

The image recognition segment accounted for around 31% of the market in 2023. Sectors like healthcare, automotive, retail, and security increasingly utilize image recognition technology to enhance operations and improve decision-making processes. In healthcare, for example, it is used to analyze medical images, enabling earlier disease detection and better patient care. U. S deep learning market held 75% share, driven by strong investments in AI research & development.

Both government and private sector funding have fostered an environment conducive to deep learning innovation. Additionally, government initiatives and favorable regulatory frameworks in Europe promote AI development, further boosting the deep learning market in that region. Many European countries are focusing on advancing AI technologies while ensuring ethical standards are maintained.

Table of Contents

Chapter 1 Methodology & Scope

• 1.1 Research design
  • 1.1.1 Research approach
  • 1.1.2 Data collection methods
• 1.2 Base estimates and calculations
  • 1.2.1 Base year calculation
  • 1.2.2 Key trends for market estimates
• 1.3 Forecast model
• 1.4 Primary research & validation
  • 1.4.1 Primary sources
  • 1.4.2 Data mining sources
• 1.5 Market definitions

Chapter 2 Executive Summary

• 2.1 Industry 360° synopsis, 2021 - 2032

Chapter 3 Industry Insights

• 3.1 Industry ecosystem analysis
  • 3.1.1 Hardware providers
  • 3.1.2 Software providers
  • 3.1.3 Service providers
  • 3.1.4 Technology providers
  • 3.1.5 End-user
• 3.2 Supplier landscape
• 3.3 Profit margin analysis
• 3.4 Deep learning architecture
• 3.5 Case studies
• 3.6 Technology & innovation landscape
• 3.7 Key news & initiatives
• 3.8 Regulatory landscape
• 3.9 Impact forces
  • 3.9.1 Growth drivers
    • 3.9.1.1 Rapid advancements in deep learning technology
    • 3.9.1.2 Rising demand for AI-powered solutions
    • 3.9.1.3 Increasing government support and initiatives
    • 3.9.1.4 Growing investment in deep learning
  • 3.9.2 Industry pitfalls & challenges
    • 3.9.2.1 Data privacy concerns
    • 3.9.2.2 High computational costs
• 3.10 Growth potential analysis
• 3.11 Porter's analysis
• 3.12 PESTEL analysis

Chapter 4 Competitive Landscape, 2023

• 4.1 Introduction
• 4.2 Company market share analysis
• 4.3 Competitive positioning matrix
• 4.4 Strategic outlook matrix

Chapter 5 Market Estimates & Forecast, By Component, 2021 - 2032 ($Bn)

• 5.1 Key trends
• 5.2 Hardware
  • 5.2.1 GPUs
  • 5.2.2 FPGAs
  • 5.2.3 ASICs
  • 5.2.4 TPUs
  • 5.2.5 Others
• 5.3 Software
• 5.4 Services
  • 5.4.1 Professional
  • 5.4.2 Managed

Chapter 6 Market Estimates & Forecast, By Organization Size, 2021 - 2032 ($Bn)

• 6.1 Key trends
• 6.2 SME
• 6.3 Large organization

Chapter 7 Market Estimates & Forecast, By Application, 2021 - 2032 ($Bn)

• 7.1 Key trends
• 7.2 Speech recognition
• 7.3 Image recognition
• 7.4 Signal recognition
• 7.5 Data processing
• 7.6 Others

Chapter 8 Market Estimates & Forecast, By End Use, 2021 - 2032 ($Bn)

• 8.1 Key trends
• 8.2 BFSI
• 8.3 IT & telecom
• 8.4 Automotive
• 8.5 Healthcare
• 8.6 Retail & e-commerce
• 8.7 Manufacturing
• 8.8 Media and entertainment
• 8.9 Others

Chapter 9 Market Estimates & Forecast, By Region, 2021 - 2032 ($Bn)

• 9.1 Key trends
• 9.2 North America
  • 9.2.1 U.S.
  • 9.2.2 Canada
• 9.3 Europe
  • 9.3.1 UK
  • 9.3.2 Germany
  • 9.3.3 France
  • 9.3.4 Spain
  • 9.3.5 Italy
  • 9.3.6 Russia
  • 9.3.7 Nordics
• 9.4 Asia Pacific
  • 9.4.1 China
  • 9.4.2 India
  • 9.4.3 Japan
  • 9.4.4 South Korea
  • 9.4.5 ANZ
  • 9.4.6 Southeast Asia
• 9.5 Latin America
  • 9.5.1 Brazil
  • 9.5.2 Mexico
  • 9.5.3 Argentina
• 9.6 MEA
  • 9.6.1 UAE
  • 9.6.2 South Africa
  • 9.6.3 Saudi Arabia

Chapter 10 Company Profiles

• 10.1 Adobe Inc.
• 10.2 Advanced Micro Devices, Inc.
• 10.3 Alibaba
• 10.4 Amazon Web Services (AWS)
• 10.5 Baidu, Inc.
• 10.6 Google LLC
• 10.7 Hewlett Packard Enterprise (HPE)
• 10.8 IBM Corporation
• 10.9 Intel Corporation
• 10.10 Meta Platforms, Inc.
• 10.11 Microsoft Corporation
• 10.12 NVIDIA Corporation
• 10.13 Oracle Corporation
• 10.14 Qualcomm
• 10.15 Salesforce
• 10.16 SAP SE
• 10.17 SenseTime
• 10.18 Tencent Holdings Ltd.
• 10.19 UiPath Inc.