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市场调查报告书
商品编码
1640699

深度学习：市场占有率分析、产业趋势与统计、成长预测（2025-2030 年）

Deep Learning - Market Share Analysis, Industry Trends & Statistics, Growth Forecasts (2025 - 2030)
出版日期: | 出版商: Mordor Intelligence | 英文 120 Pages | 商品交期: 2-3个工作天内

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简介目录

深度学习市场规模在2025年预估为348.9亿美元，预计2030年将达到1,951.6亿美元，预测期间（2025-2030年）复合年增长率为41.1%。

深度学习-市场-IMG1

深度学习作为机器学习（ML）的一个分支，为语音辨识和影像识别等多项人工智慧任务带来了突破。此外，自动化预测分析的能力也是推动机器学习热情的动力。增加对产品开发和改进、流程优化和功能工作流程、销售优化等支援力度，推动各行各业的公司纷纷投资深度学习应用。此外，现代机器学习方法显着提高了模型准确性，并推动了用于图像分类和文字翻译等应用的新型神经网路的开发。

主要亮点

  • 资料中心容量的不断增加、高运算能力以及无需人工输入即可执行任务的能力等技术进步正在吸引大量关注。深度学习产业的成长也受到众多领域云端运算技术的快速应用的推动。
  • 目前，有多项发展正在推动深度学习的发展。据 SAS 称，演算法的改进正在提升深度学习方法的效能。越来越多的资料，包括来自物联网 (IoT) 的串流资料以及来自社交媒体和医生笔记的文字资料，正在支援建立具有多个深层的神经网路。鑑于深度学习演算法的迭代特性（增加层数会增加复杂性），解决深度学习问题需要强大的运算能力。运行深度学习演算法的硬体还必须支援训练网路所需的大量资料。
  • 图形处理单元（GPU）和分散式云端处理的运算进步为使用者提供了令人难以置信的运算能力。此项开发由 NVIDIA、Intel 和 AMD 等硬体供应商主导，除其他功能外，它还提高了运算速度并支援一些最常用和新兴的技术，包括Tensorflow、Cognitive Toolkit（微软）、Chainer、Caffe 和PyTorch 。因此，开放原始码深度学习功能在整个企业中变得越来越普遍。这些开放原始码框架使用户能够有效率且快速地建立机器学习模型。
  • 深度学习在充分发挥其潜力之前，还有许多重大限制需要克服，包括黑箱问题、人口不足、缺乏情境理解、资料要求和计算强度，这些都可能对市场产生影响。
  • 因此，COVID-19 对科技业产生了难以置信的影响。深度学习演算法正被用于根据胸部X光片和电脑断层扫描等临床影像辅助诊断和检测COVID-19病例。医疗保健领域对 MRI 分析工具的需求不断增长，正在推动深度学习市场的崛起。

深度学习市场趋势

深度学习在零售业的广泛应用推动市场

  • 近年来，零售业的业务基础发生了重大转变，许多知名品牌选择减少现场服务，转而支持线上服务。为了维持运营，零售商必须满足顾客的期望并采取相应行动，否则可能会失去顾客的忠诚度。为了实现这一目标，零售商必须拥抱新兴技术。深度学习使零售商能够以前所未有的方式实现客户体验自动化并简化流程。例如，线上场景中的货架分析可以提供有用的产品推荐和更快的分类，帮助客户更快地做出正确的选择并获得更多支援。
  • 沃尔玛等线上零售商开始使用人工智慧从客户那里获取产品推荐，但他们才刚开始充分利用该技术的全部潜力。透过深度学习，零售商可以真正利用人工智慧的力量来优化用户体验并自动执行耗时的任务。例如，线上零售商可以使用深度学习自动标记视觉资料，以改善用户体验的各个方面。人工智慧可用于优化搜寻、为搜寻查询返回更好的结果、提高产品图像的品质等等。未来零售商将能够利用深度学习技术快速收集资料并自动分析资讯。
  • 雪花计算《哈佛商业评论》的一项研究发现，选择以资料为依据做出决策的零售商生存的时间更长。毫无疑问，零售业正在迅速变得更加以资料为导向。调查发现，89% 的零售商表示，更深入了解顾客期望是其关键目标。零售领域的深度学习使用足够复杂和先进的模型来应对机器学习模型失败的挑战。例如，零售应用程式模型中的深度学习足够智能，能够理解更大萤幕智慧型手机的发布可能会蚕食平板电脑的销售。当资料缺失时，零售业的深度学习可以从商品销售缓慢或缺货的模式中学习。
  • 如今，需求预测和客户智慧只是零售商和消费品公司使用智慧自动化完成的不同内部活动的两个例子。但未来三年，经营团队打算将智慧自动化和深度学习融入更复杂的业务中。这些步骤需要更大的资料、外部协作和额外的系统连接。在此期间，渗透率预计将在整个价值链的组织领域中增加到 70% 以上。
  • 例如，运动鞋、服装和设备製造商耐吉 (Nike) 已经创建了一个系统，让消费者设计自己的鞋子并在离开商店后穿着。参加 Nike Maker Experience 的顾客将穿上一双裸色 Nike Presto X 运动鞋，并使用语音指令进行客製化。该技术利用扩增实境、物件追踪和投影系统向购物者展示成品鞋。

预计北美将占很大份额

  • 预计北美将占据全球深度学习市场的很大份额。这是由于资料量的持续成长以及将 DL 整合到以企业消费者为中心的解决方案中的需求预期增加。更加关注预测与客户行为和业务相关的关键趋势和见解，正在推动使用人工智慧和巨量资料来推动价值并提供个人化体验，这是重要的驱动力。例如，Netflix 已经基于 Scala 等 JVM 语言建构了机器学习平台。该平台帮助观众打破先入为主的观念，发现他们最初可能不会选择的节目。
  • 美国各机构现在严重依赖人工智慧和机器学习技术来提高任务效率，扩大劳动力能力，防止浪费、诈欺和滥用，并提高业务效率。人工智慧技术的进步、越来越多的人工智慧使用案例和应用以及不断扩展的商业解决方案都在推动人工智慧的扩张，使其不再局限于美国国家航空航天局和能源部等专业机构的研究和开发工作。
  • 美国运输部製定了新的安全法规，以消除车辆后方的盲点并帮助您看到车辆后方的人。根据美国公路交通安全运输部的统计，所有车辆的追撞事故共导致约 292 人死亡，18,000 人受伤。预计此类法规将推动 ADAS 的采用，从而为该地区的深度学习市场提供机会。此外，该地区汽车製造商对开发先进解决方案的投资也不断增加，从而推动了市场成长。
  • 此外，美国公司也不断扩大研发力度，开发新产品。例如，Google LLC 于 2022 年 12 月宣布推出新工具，让使用者能够在 Google Sheets 中开发人工智慧模型。该工具名为 Simple ML，目前处于测试阶段。它作为 Google Sheets附加元件提供，用户可以免费下载。

深度学习行业概览

深度学习市场比较分散，由少数在巨量资料和分析平台方面拥有丰富产业经验的大公司组成，例如 IBM、Google和微软。其他新参与企业也进入了市场，并成功增加了各行业的深度学习使用案例数量。对市场产生重大影响的着名新参与企业包括 H2O.ai、KNIME 和 Dataiku。

2023 年 11 月 - 作为通讯业机器学习(ML) 技术和人工智慧(AI) 发展的重要一步，Telenor 和爱立信签署了战略合作伙伴关係，旨在提高行动网路的能源效率，同时不影响连接品质。两家公司签署了为期三年的合作谅解备忘录（MoU），旨在探索、开发和测试先进的 AI/ML 解决方案。

2022 年 10 月，Zendesk Inc. 宣布了新的 AI 解决方案、Intelligent Triage 和 Smart Assist。

2022 年 9 月，计算科学和人工智慧公司 Altair 宣布收购高级资料分析和机器学习 (ML) 软体领导者 RapidMiner。透过此次收购，Altair 增强了其端到端资料分析 (DA) 产品组合。

其他福利

  • Excel 格式的市场预测 (ME) 表
  • 3 个月的分析师支持

目录

第 1 章 简介

  • 研究假设和市场定义
  • 研究范围

第二章调查方法

第三章执行摘要

第四章 市场洞察

  • 市场概况
  • 产业吸引力－波特五力分析
    • 供应商的议价能力
    • 消费者议价能力
    • 新进入者的威胁
    • 替代品的威胁
    • 竞争对手之间的竞争
  • 产业相关人员分析
  • COVID-19 对深度学习市场的影响评估

第五章 市场动态

  • 市场驱动因素
    • 运算能力的不断提高以及大量非结构化资料的存在
    • 继续努力将深度学习融入消费群方案
    • 零售业对深度学习的广泛应用推动了市场
  • 市场挑战
    • 营运和基础设施问题，例如硬体复杂性和对技术纯熟劳工的需求
  • 市场机会
  • 深度学习技术的演变
  • 主要机器学习库分析

第六章 市场细分

  • 透过提供
    • 硬体
    • 软体和服务
  • 按最终用户产业
    • BFSI
    • 零售
    • 製造业
    • 卫生保健
    • 车
    • 通讯与媒体
    • 其他最终用户产业
  • 按应用
    • 影像识别
    • 讯号识别
    • 资料处理
    • 其他应用
  • 按地区
    • 北美洲
    • 欧洲
    • 亚太地区
    • 世界其他地区

第七章 竞争格局

  • 公司简介
    • Facebook Inc.
    • Google
    • Amazon Web Services Inc
    • SAS Institute Inc
    • Microsoft Corporation
    • IBM Corp
    • Advanced Micro Devices Inc
    • Intel Corp
    • NVIDIA Corp
    • Rapidminer Inc

第八章投资分析

第九章：市场的未来

简介目录
Product Code: 57207

The Deep Learning Market size is estimated at USD 34.89 billion in 2025, and is expected to reach USD 195.16 billion by 2030, at a CAGR of 41.1% during the forecast period (2025-2030).

Deep Learning - Market - IMG1

Deep learning, a subfield of machine learning (ML), led to breakthroughs in several artificial intelligence tasks, including speech recognition and image recognition. Furthermore, the ability to automate predictive analytics is leading to the hype for ML. Factors such as enhanced support in product development and improvement, process optimization and functional workflows, and sales optimization, among others, have been driving enterprises across industries to invest in deep learning applications. Furthermore, the latest machine-learning approaches have significantly improved the accuracy of models, and new classes of neural networks have been developed for applications like image classification and text translation.

Key Highlights

  • Technological advances, such as increasing data center capacity, high computing power and the ability to carry out tasks without human input, have attracted significant attention. In addition, the growth of the deep learning industry is fueled by rapidly adopting cloud computing technology across a number of sectors.
  • Several developments are now advancing deep learning. According to SAS, improvements in algorithms have boosted the performance of deep learning methods. The increasing amount of data volumes has been supportive of the building of neural networks with several deep layers, including streaming data from the Internet of Things (IoT) and textual data from social media and physicians' notes. A significant amount of computational power is essential to solve deep learning problems, considering the iterative nature of deep learning algorithms-their complexity increases as the number of layers increases. The hardware running deep learning algorithms also needs to support the large volumes of data required to train the networks.
  • Computational advances in graphic processing units (GPUs) and distributed cloud computing have put incredible computing power at the users' disposal. This development is led by hardware providers, such as NVIDIA, Intel, and AMD, among others, which have been improving the computational speeds among other features and making them compatible with most-used open-source platforms, such as Tensorflow, Cognitive Toolkit (Microsoft), Chainer, Caffe, and PyTorch, among others. Therefore, 'open-sourcing deep learning capabilities' have become increasingly popular across enterprises. These open-source frameworks enable users to build machine-learning models efficiently and quickly.
  • Deep learning has a number of serious limitations that need to be overcome before it can achieve its full potential, such as the black box problem, overpopulation, lack of contextual understanding, data requirements and computational intensity, which might effect market
  • As a result, COVID-19 has had an excellent impact for the technology sector. Deep learning algorithms have been employed for assisting diagnosis and detection of COVIDE-19 cases based on clinical images, e.g. chest Xray or CT scans. The growing demand for MRI analysis tools within the healthcare sector which has led to a rise in the depth learning market.

Deep Learning Market Trends

Growing Use of Deep Learning in Retail Sector is Driving the Market

  • The retail industry has seen a drastic shift in its base of operations in recent times, with many notable brands choosing to reduce the number of onsite offerings in favor of online service. For retailers to remain viable, they need to meet customer expectations, act accordingly, or risk losing loyalty. It is also becoming vital for retailers to adopt burgeoning technologies to make this a reality. Deep learning allows retailers to automate customer experience and streamline processes in a way hitherto unknown. For example, shelf analytics in online scenarios can help with useful recommendations of merchandise and quick classification, which allows customers to make correct choices with more support more quickly.
  • Online retailers such as Walmart are starting to use AI to get product recommendations from customers but are just barely utilizing the full potential the technology can offer. By using deep learning, retailers can truly harness the power of AI to optimize user experiences and automate time-consuming tasks. For instance, online retailers can use Deep Learning to automatically tag visual data to improve many facets of the user experience. They can use AI to refine the search and return better results to search queries or enhance product images' quality, especially low-quality product photos using color enhancement. Moving forward, retailers can quickly gather data and analyze information automatically using Deep Learning technology.
  • A study by Snowflake Computing Harvard Business Review points out that retailers who choose to make data-driven decisions have survived longer. Undoubtedly, retail is rapidly becoming extremely data-oriented. As per the same study, 89% of retailers consider gaining improved insights into customer expectations a significant goal. The models that Deep learning in retail utilizes are sophisticated and advanced enough to handle the challenges that machine learning models fail at. For example, deep learning in retail application models is intelligent enough to understand that the release of smartphones with larger screens can eat up tablets' sales. In the case of missing data, deep learning in retail could learn from patterns whether an item isn't selling or is out of stock.
  • These days, demand forecasting and customer intelligence are only two examples of distinct internal activities that retail and consumer products companies utilize intelligent automation to carry out. Executives, however, intend to integrate intelligent automation and deep learning into more intricate operations over the course of the following three years. These procedures call for larger data sets, external cooperation, and extra system connections. The estimated penetration is anticipated to increase to above 70% across organizational domains that span the value chain over that period.
  • For instance, sports footwear, apparel, and equipment manufacturer Nike Inc. has created a system that allows consumers to design their own shoes and wear them after they leave the store-utilizing the fresh automated system. Customers who participate in The Nike Maker Experience put on a pair of unadorned Nike Presto X sneakers and customize them via voice commands. The technology shows the buyer the created shoes using augmented reality, object tracking, and projection systems.

North America is Expected to Hold Major Share

  • North America is expected to have a significant share in the global deep learning market, owing to the sustained rise in considerable data volume, coupled with the anticipated increase in the demand for the integration of DL in consumer-centric solutions of enterprises. The growing emphasis on predicting the key trends and insights related to customer behavior and operations has been a critical driver for significant enterprises to veer toward the use of AI and big data for driving value and offering a personalized experience. For instance, Netflix built a machine learning platform based on JVM languages, like Scala. The platform helps break viewers' preconceived notions and find shows that they might not have initially chosen.
  • In order to increase mission effectiveness, stretch workforce capacity, prevent waste, fraud, and abuse, and increase operational efficiency, agencies in the US now rely heavily on artificial intelligence and machine learning technology. The advancement of AI technology, a rising number of AI use cases and applications, and the expansion of commercial solutions have all helped to expand the usage of AI outside the R&D activities at specialized organizations like NASA and the Department of Energy.
  • The United States Department of Transportation formed a new safety regulation to help eliminate blind zones behind vehicles and view people present behind vehicles. According to National Highway Traffic Safety Administration stats, around 292 fatalities and 18,000 injuries occur due to back-over crashes involving all vehicles. Such regulations are anticipated to encourage the adoption of ADAS, thereby offering opportunities for the region's deep learning market. Furthermore, the region is also seeing an increase in investments from automakers to develop advanced solutions, driving the growth of the market.
  • Moreover, companies in the US are continuously expanding on their R&D to develop new products. For instance, in December 2022, Google LLC announced the launch of a new tool in order to enable users to develop artificial intelligence models in Google Sheets. The tool, dubbed Simple ML, is available in beta. It's provided as an add-on to Google Sheets that users can download at no charge.

Deep Learning Industry Overview

The deep learning market is fragmented as it consists of several large players, such as IBM, Google, and Microsoft, among others, with substantial industrial experience in big data/analytical platforms. Other new entrants also have been making their way into the market and have been successfully increasing the number of use cases of deep learning across industries. Prominent new entrants that have made a significant impact on the market include H2O.ai, KNIME, and Dataiku.

In November 2023 - In a step towards advancing the realm of machine learning (ML) technologies and artificial intelligence (AI) within the telecommunications industry, Telenor and Ericsson have signed an (MoU) for a three-year collaboration that aims to explore, develop, and test advanced AI/ML solutions towards enhancing energy efficiency without compromising on the quality of connectivity in mobile networks.

In October 2022, Zendesk Inc. announced the launch of a new AI solution, Intelligent Triage and Smart Assist, empowering businesses to triage customer support requests automatically and access valuable data at scale.

In September 2022, Altair, a company providing computational science and artificial intelligence, announced the acquisition of rapid miner, a leader in advanced data analytics and machine learning (ML) software. With this acquisition, Altair's looking forward to strengthening its end-to-end data analytics (DA) portfolio.

Additional Benefits:

  • The market estimate (ME) sheet in Excel format
  • 3 months of analyst support

TABLE OF CONTENTS

1 INTRODUCTION

  • 1.1 Study Assumptions and Market Definition
  • 1.2 Scope of the Study

2 RESEARCH METHODOLOGY

3 EXECUTIVE SUMMARY

4 MARKET INSIGHTS

  • 4.1 Market Overview
  • 4.2 Industry Attractiveness - Porter's Five Forces Analysis
    • 4.2.1 Bargaining Power of Suppliers
    • 4.2.2 Bargaining Power of Consumers
    • 4.2.3 Threat of New Entrants
    • 4.2.4 Threat of Substitute Products
    • 4.2.5 Intensity of Competitive Rivalry
  • 4.3 Industry Stakeholder Analysis
  • 4.4 Assessment of Impact of COVID-19 on Deep Learning Market

5 MARKET DYNAMICS

  • 5.1 Market Drivers
    • 5.1.1 Increasing Computing Power, coupled with the Presence of Large Unstructured Data
    • 5.1.2 Ongoing Efforts toward the Integration of DL in Consumer-based Solutions
    • 5.1.3 Growing Use of Deep Learning in Retail Sector is Driving the Market
  • 5.2 Market Challenges
    • 5.2.1 Operational and Infrastructural Concerns, such as Hardware Complexity and Need for Skilled Workforce
  • 5.3 Market Opportunities
  • 5.4 Technology Evolution of Deep Learning
  • 5.5 Analysis of Key Machine Learning Libraries

6 MARKET SEGMENTATION

  • 6.1 Offering
    • 6.1.1 Hardware
    • 6.1.2 Software and Services
  • 6.2 End-User Industry
    • 6.2.1 BFSI
    • 6.2.2 Retail
    • 6.2.3 Manufacturing
    • 6.2.4 Healthcare
    • 6.2.5 Automotive
    • 6.2.6 Telecom and Media
    • 6.2.7 Other End-user Industries
  • 6.3 Application
    • 6.3.1 Image Recognition
    • 6.3.2 Signal Recognition
    • 6.3.3 Data Processing
    • 6.3.4 Other Applications
  • 6.4 Geography
    • 6.4.1 North America
    • 6.4.2 Europe
    • 6.4.3 Asia-Pacific
    • 6.4.4 Rest of the World

7 COMPETITIVE LANDSCAPE

  • 7.1 Company Profiles
    • 7.1.1 Facebook Inc.
    • 7.1.2 Google
    • 7.1.3 Amazon Web Services Inc
    • 7.1.4 SAS Institute Inc
    • 7.1.5 Microsoft Corporation
    • 7.1.6 IBM Corp
    • 7.1.7 Advanced Micro Devices Inc
    • 7.1.8 Intel Corp
    • 7.1.9 NVIDIA Corp
    • 7.1.10 Rapidminer Inc

8 INVESTMENT ANALYSIS

9 FUTURE OF THE MARKET