深度学习市场机会、成长动力、产业趋势分析以及 2024 年至 2032 年预测

2023年全球深度学习市场估值为198亿美元，预计2024年至2032年复合年增长率为30.4%。公司正在寻求提高效率、降低成本并最大限度地减少人为错误，而深度学习技术为自动化复杂任务提供了有效的解决方案。云端运算的兴起进一步推动了深度学习市场的发展。云端平台提供可扩展且灵活的资源，使企业无需大量初始硬体投资即可存取高效能运算。

这使得公司可以更轻鬆地实施深度学习解决方案、管理大型资料集、训练复杂的模型以及快速部署应用程式。 AWS、Google Cloud 和 Microsoft Azure 等领先的云端供应商提供专业的深度学习服务。这些平台提供了预先建置的框架和工具，可以简化开发过程，推动创新并增加深度学习技术的采用。随着越来越多的公司采用云端运算进行资料处理，对深度学习解决方案的需求将持续成长。

市场根据组件分为硬体、软体和服务。 2023 年，软体领域占据了超过 30% 的市场份额，预计到 2032 年将超过 800 亿美元。这些工具使开发人员可以更轻鬆地建置、训练和部署神经网路。从应用来看，深度学习市场分为影像辨识、语音辨识、讯号辨识、资料处理等。

到 2023 年，影像辨识领域约占市场的 31%。例如，在医疗保健领域，它用于分析医学影像，从而实现更早的疾病检测和更好的病患照护。在人工智慧研发的强劲投资推动下，美国深度学习市场占了 75% 的份额。

政府和私部门的资金都创造了有利于深度学习创新的环境。此外，欧洲的政府措施和有利的监管框架促进了人工智慧的发展，进一步推动了该地区的深度学习市场。许多欧洲国家都专注于推动人工智慧技术，同时确保维持道德标准。

目录

第 1 章：方法与范围

第 2 章：执行摘要

第 3 章：产业洞察

• 产业生态系统分析
  • 硬体提供者
  • 软体供应商
  • 服务提供者
  • 技术提供者
  • 终端用户
• 供应商格局
• 利润率分析
• 深度学习架构
• 案例研究
• 技术与创新格局
• 重要新闻和倡议
• 监管环境
• 衝击力
  • 成长动力
    • 深度学习技术的快速进步
    • 对人工智慧驱动的解决方案的需求不断增长
    • 加大政府支持与倡议
    • 深度学习投资不断增加
  • 产业陷阱与挑战
    • 资料隐私问题
    • 计算成本高
• 成长潜力分析
• 波特的分析
• PESTEL分析

第 4 章：竞争格局

• 介绍
• 公司市占率分析
• 竞争定位矩阵
• 战略展望矩阵

第 5 章：市场估计与预测：按组成部分，2021 - 2032 年

• 主要趋势
• 硬体
  • GPU
  • FPGA
  • ASIC
  • TPU
  • 其他的
• 软体
• 服务
  • 专业的
  • 託管

第 6 章：市场估计与预测：依组织规模，2021 - 2032 年

• 主要趋势
• 中小企业
• 大型组织

第 7 章：市场估计与预测：依应用分类，2021 - 2032

• 主要趋势
• 语音辨识
• 影像辨识
• 讯号识别
• 资料处理
• 其他的

第 8 章：市场估计与预测：依最终用途，2021 - 2032 年

• 主要趋势
• BFSI
• 资讯科技与电信
• 汽车
• 卫生保健
• 零售与电子商务
• 製造业
• 媒体与娱乐
• 其他的

第 9 章：市场估计与预测：按地区，2021 - 2032

• 主要趋势
• 北美洲
  • 我们
  • 加拿大
• 欧洲
  • 英国
  • 德国
  • 法国
  • 西班牙
  • 义大利
  • 俄罗斯
  • 北欧人
• 亚太地区
  • 中国
  • 印度
  • 日本
  • 韩国
  • 澳新银行
  • 东南亚
• 拉丁美洲
  • 巴西
  • 墨西哥
  • 阿根廷
• MEA
  • 阿联酋
  • 南非
  • 沙乌地阿拉伯

第 10 章：公司简介

• 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.