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市場調查報告書
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1640699

深度學習：市場佔有率分析、產業趨勢與統計、成長預測（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個工作天內

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簡介目錄

深度學習市場規模在2025年預估為348.9億美元，預計2030年將達到1,951.6億美元，預測期間（2025-2030年）複合年成長率為41.1%。

深度學習-市場-IMG1

深度學習作為機器學習（ML）的一個分支，為語音辨識和影像識別等多項人工智慧任務帶來了突破。此外，自動化預測分析的能力也是推動機器學習熱情的動力。增加對產品開發和改進、流程最佳化和功能工作流程、銷售最佳化等支援力度，推動各行各業的公司紛紛投資深度學習應用。此外，現代機器學習方法顯著提高了模型準確性，並推動了用於圖像分類和文字翻譯等應用的新型神經網路的開發。

主要亮點

  • 資料中心容量的不斷增加、高運算能力以及無需人工輸入即可執行任務的能力等技術進步正在吸引大量關注。深度學習產業的成長也受到眾多領域雲端運算技術的快速應用的推動。
  • 目前，有多項發展正在推動深度學習的發展。據 SAS 稱，演算法的改進正在提升深度學習方法的效能。越來越多的資料，包括來自物聯網 (IoT) 的串流資料以及來自社交媒體和醫生筆記的文字資料，正在支援建立具有多個深層的神經網路。鑑於深度學習演算法的迭代特性（增加層數會增加複雜性），解決深度學習問題需要強大的運算能力。運行深度學習演算法的硬體還必須支援訓練網路所需的大量資料。
  • 圖形處理單元（GPU）和分散式雲端處理的運算進步為使用者提供了令人難以置信的運算能力。此項開發由 NVIDIA、Intel 和 AMD 等硬體供應商主導，除其他功能外，它還提高了運算速度並支援一些最常用和新興的技術，包括Tensorflow、Cognitive Toolkit（微軟）、Chainer、Caffe 和PyTorch 。因此，開放原始碼深度學習功能在整個企業中變得越來越普遍。這些開放原始碼框架使用戶能夠有效率且快速地建立機器學習模型。
  • 深度學習在充分發揮其潛力之前，還有許多重大限制需要克服，包括黑箱問題、人口不足、缺乏情境理解、資料要求和計算強度，這些都可能對市場產生影響。
  • 因此，COVID-19 對科技業產生了難以置信的影響。深度學習演算法正被用於根據胸部X光片和電腦斷層掃描等臨床影像輔助診斷和檢測COVID-19病例。醫療保健領域對 MRI 分析工具的需求不斷成長，正在推動深度學習市場的崛起。

深度學習市場趨勢

深度學習在零售業的廣泛應用推動市場

  • 近年來，零售業的業務基礎發生了重大轉變，許多知名品牌選擇減少現場服務，轉而支持線上服務。為了維持營運，零售商必須滿足顧客的期望並採取相應行動，否則可能會失去顧客的忠誠度。為了實現這一目標，零售商必須擁抱新興技術。深度學習使零售商能夠以前所未有的方式實現客戶體驗自動化並簡化流程。例如，線上場景中的貨架分析可以提供有用的產品推薦和更快的分類，幫助客戶更快地做出正確的選擇並獲得更多支援。
  • 沃爾瑪等線上零售商開始使用人工智慧從客戶那裡獲取產品推薦，但他們才剛開始充分利用該技術的全部潛力。透過深度學習，零售商可以真正利用人工智慧的力量來最佳化用戶體驗並自動執行耗時的任務。例如，線上零售商可以使用深度學習自動標記視覺資料，以改善用戶體驗的各個方面。人工智慧可用於最佳化搜尋、為搜尋查詢返回更好的結果、提高產品圖像的品質等等。未來零售商將能夠利用深度學習技術快速收集資料並自動分析資訊。
  • 雪花計算《哈佛商業評論》的一項研究發現，選擇以資料為依據做出決策的零售商生存的時間更長。毫無疑問，零售業正在迅速變得更加以資料為導向。調查發現，89% 的零售商表示，更深入了解顧客期望是其關鍵目標。零售領域的深度學習使用足夠複雜和先進的模型來應對機器學習模型失敗的挑戰。例如，零售應用程式模型中的深度學習足夠智慧，能夠理解更大螢幕智慧型手機的發布可能會蠶食平板電腦的銷售。當資料缺失時，零售業的深度學習可以從商品銷售緩慢或缺貨的模式中學習。
  • 如今，需求預測和客戶智慧只是零售商和消費品公司使用智慧自動化完成的不同內部活動的兩個例子。但未來三年，經營團隊打算將智慧自動化和深度學習融入更複雜的業務中。這些步驟需要更大的資料、外部協作和額外的系統連接。在此期間，滲透率預計將在整個價值鏈的組織領域中增加到 70% 以上。
  • 例如，運動鞋、服裝和設備製造商耐吉 (Nike) 已經創建了一個系統，讓消費者設計自己的鞋子並在離開商店後穿著。參加 Nike Maker Experience 的顧客將穿上一雙裸色 Nike Presto X 運動鞋，並使用語音指令進行客製化。該技術利用擴增實境、物件追蹤和投影系統向購物者展示成品鞋。

預計北美將佔很大佔有率

  • 預計北美將佔據全球深度學習市場的很大佔有率。這是由於資料量的持續成長以及將 DL 整合到以企業消費者為中心的解決方案中的需求預期增加。更加關注預測與客戶行為和業務相關的關鍵趨勢和見解，正在推動使用人工智慧和巨量資料來推動價值並提供個人化體驗，這是重要的驅動力。例如，Netflix 已經基於 Scala 等 JVM 語言建構了機器學習平台。該平台幫助觀眾打破先入為主的觀念，發現他們最初可能不會選擇的節目。
  • 美國各機構現在嚴重依賴人工智慧和機器學習技術來提高任務效率，擴大勞動力能力，防止浪費、詐欺和濫用，並提高業務效率。人工智慧技術的進步、越來越多的人工智慧使用案例和應用以及不斷擴展的商業解決方案都在推動人工智慧的擴張，使其不再局限於美國國家航空航太局和能源部等專業機構的研究和開發工作。
  • 美國運輸部製定了新的安全法規，以消除車輛後方的盲點並幫助您看到車輛後方的人。根據美國公路交通安全運輸部的統計，所有車輛的追撞事故共導致約 292 人死亡，18,000 人受傷。預計此類法規將推動 ADAS 的採用，從而為該地區的深度學習市場提供機會。此外，該地區汽車製造商對開發先進解決方案的投資也不斷增加，從而推動了市場成長。
  • 此外，美國公司也不斷擴大研發力度，開發新產品。例如，Google LLC 於 2022 年 12 月宣布推出新工具，讓使用者能夠在 Google Sheets 中開發人工智慧模型。該工具名為 Simple ML，目前處於測試階段。它作為 Google Sheets附加元件提供，用戶可以免費下載。

深度學習行業概覽

深度學習市場比較分散，由少數在巨量資料和分析平台方面擁有豐富產業經驗的大公司組成，例如 IBM、Google和微軟。其他新參與企業也進入了市場，並成功增加了各行業的深度學習使用案例數量。對市場產生重大影響的著名新參與企業包括 H2O.ai、KNIME 和 Dataiku。

2023 年 11 月 - 作為通訊業機器學習(ML) 技術和人工智慧(AI) 發展的重要一步，Telenor 和愛立信簽署了戰略合作夥伴關係，旨在提高行動網路的能源效率，同時不影響連接品質。兩家公司簽署了為期三年的合作合作備忘錄（MoU），旨在探索、開發和測試先進的 AI/ML 解決方案。

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

2022 年 9 月，計算科學和人工智慧公司 Altair 宣布收購高級資料分析和機器學習 (ML) 軟體領導者 RapidMiner。透過此次收購，Altair 增強了其端到端資料分析 (DA) 產品組合。

其他福利

  • Excel 格式的市場預測 (ME) 表
  • 3 個月的分析師支持

目錄

第 1 章 簡介

  • 研究假設和市場定義
  • 研究範圍

第2章調查方法

第3章執行摘要

第4章 市場洞察

  • 市場概況
  • 產業吸引力－波特五力分析
    • 供應商的議價能力
    • 消費者議價能力
    • 新進入者的威脅
    • 替代品的威脅
    • 競爭對手之間的競爭
  • 產業相關人員分析
  • COVID-19 對深度學習市場的影響評估

第5章 市場動態

  • 市場促進因素
    • 運算能力的不斷提高以及大量非結構化資料的存在
    • 繼續努力將深度學習融入消費群方案
    • 零售業對深度學習的廣泛應用推動了市場
  • 市場挑戰
    • 營運和基礎設施問題，例如硬體複雜性和對技術純熟勞工的需求
  • 市場機會
  • 深度學習技術的演變
  • 主要機器學習庫分析

第6章 市場細分

  • 透過提供
    • 硬體
    • 軟體和服務
  • 按最終用戶產業
    • BFSI
    • 零售
    • 製造業
    • 衛生保健
    • 車
    • 通訊與媒體
    • 其他最終用戶產業
  • 按應用
    • 影像識別
    • 訊號識別
    • 資料處理
    • 其他應用
  • 按地區
    • 北美洲
    • 歐洲
    • 亞太地區
    • 世界其他地區

第7章 競爭格局

  • 公司簡介
    • 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8章投資分析

第9章：市場的未來

簡介目錄
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