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市場調查報告書
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1432970

深度學習：市場佔有率分析、產業趨勢與統計、成長預測（2024-2029）

Deep Learning - Market Share Analysis, Industry Trends & Statistics, Growth Forecasts (2024 - 2029)
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

2024年深度學習市場規模估計為247.3億美元，預計到2029年將達到1383.6億美元，在預測期間（2024-2029年）以41.10%的複合年增長率增長。

深度學習 - 市場

深度學習是機器學習 (ML) 的一個子領域，它在語音辨識和影像識別等多項人工智慧任務中取得了突破性進展。此外，自動化預測分析的能力也引發了機器學習的炒作。產品開發和改進支援的增加、流程最佳化和功能工作流程以及銷售最佳化等因素正在推動各行業的公司投資深度學習應用程式。此外，現代機器學習方法顯著提高了模型準確性，並為影像分類和文字翻譯等應用開發了一類新型神經網路。

主要亮點

  • 資料中心容量的增加、運算能力的提高以及無需人工輸入即可執行任務的能力等技術進步正受到廣泛關注。此外，雲端運算技術在許多領域的快速採用也推動了深度學習產業的成長。
  • 目前有多項進展正在推動深度學習的發展。據 SAS 稱，演算法改進提高了深度學習技術的性能。越來越多的資料有助於建立具有多個深層的神經網路，例如來自物聯網 (IoT) 的串流資料或來自社交媒體或醫生筆記的文字資料。鑑於深度學習演算法的迭代性質（複雜性隨著層數的增加而增加），大量的運算能力對於解決深度學習問題至關重要。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) 和分散式雲端運算的運算進步為使用者提供了巨大的運算能力。這項開發由 NVIDIA、Intel 和 AMD 等硬體供應商主導，旨在提高運算速度，並與 Tensorflow 和 Cognitive Toolkit 等最受歡迎的開放原始碼平台整合等功能，實現相容性。微軟、Chainer、Caffe、PyTorch 等因此，深度學習功能的開放原始碼在公司中變得越來越普及。這些開放原始碼框架允許使用者有效率、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需要克服嚴峻的課題，包括黑盒子問題、人口過剩、缺乏上下文理解、資料要求以及可能影響市場的計算強度等，並存在許多限制。
  • 因此，COVID-19 對科技業產生了重大影響。深度學習演算法被用來根據胸部 X 光和電腦斷層掃描等臨床影像來幫助診斷和檢測 COVIDE-19 病例。由於醫療保健領域對 MRI 分析工具的需求不斷增加，深度學習市場正在擴大。

深度學習市場趨勢

零售領域擴大使用深度學習來推動市場發展

  • 零售業最近的營運基礎發生了巨大變化，許多知名品牌選擇減少現場產品供應的數量，轉而採用線上服務。為了保持活力，零售商必須滿足客戶的期望並採取相應行動，否則就有失去忠誠度的風險。對於零售商來說，實施快速發展的技術來實現這一目標也很重要。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幫助提供有用的推薦和產品的快速分類，讓客戶在更多支援下更快地做出正確的選擇。
  • 沃爾瑪等線上零售商已開始使用人工智慧從客戶那裡獲取產品推薦，但才剛開始充分利用該技術所能提供的潛力。透過使用深度學習，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。您可以使用人工智慧來最佳化搜尋並為您的搜尋查詢返回更好的結果，並使用色彩校正來提高產品圖像的質量，尤其是低品質的產品照片。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指出，選擇根據資料做出決策的零售商生存時間更長。毫無疑問，零售業正迅速變得極為資料化。根據同一項研究，89% 的零售商認為提高對顧客期望的洞察力是一個重要目標。深度學習在零售業中使用的模型非常複雜且先進，足以應對機器學習模型失敗的課題。例如，零售應用程式模型中的深度學習足夠智慧，可以理解大螢幕智慧型手機的發布可能會侵蝕平板電腦的銷售。當資料遺失時，零售業的深度學習可以從模式中了解商品是否沒有銷售或缺貨。
  • 如今，需求預測和客戶智慧只是零售和消費品公司利用智慧自動化執行的不同內部活動的兩個例子。但在未來三年內，經營團隊計劃將智慧自動化和深度學習整合到更複雜的業務中。這些步驟需要更大的資料集、外部協作和額外的系統連接。預計在此期間，跨價值鏈的組織領域的普及將增加至 70% 以上。
  • 例如，運動鞋、服裝和設備製造商耐吉公司利用尖端自動化技術創建了一個系統，讓消費者設計自己的鞋子並在離開商店後穿上它們。參與 Nike Maker Experience 的客戶穿著普通的 Nike Presto X 運動鞋，並使用語音指令進行客製化。該技術使用擴增實境、物件追蹤和投影系統向買家展示製作的鞋子。

預計北美將佔據主要佔有率

  • 由於資料量預計將持續成長，以及企業以消費者為中心的解決方案中對 DL 整合的需求不斷增加，預計北美將在全球深度學習市場中佔據主要佔有率。預測與客戶行為和業務相關的關鍵趨勢和洞察變得越來越重要，這正在推動領先公司使用人工智慧和巨量資料來驅動價值並提供個人化體驗，這是改變方向的重要驅動力。例如，Netflix基於Scala等JVM語言建構了機器學習平台。該平台幫助觀眾突破先入為主的觀念，發現他們最初可能沒有選擇的節目。
  • 為了提高任務效率、擴大勞動力能力、防止浪費、詐騙和濫用以及提高業務效率，美國政府機構現在嚴重依賴人工智慧和機器學習技術。人工智慧技術的進步、人工智慧用例和應用的激增以及商業解決方案的擴展都有助於將人工智慧的使用擴展到美國太空總署和能源部等專門機構的研發工作之外。
  • 美國運輸部頒布了新的安全法規，以消除車輛後方的盲點並提高車輛後方人員的能見度。根據美國公路交通安全管理局的統計，在涉及所有車輛的倒車事故中，約有 292 人死亡、18,000 人受傷。此類法規預計將推動 ADAS 的採用，從而為該地區的深度學習市場提供機會。此外，該地區汽車製造商也不斷增加投資來開發先進的解決方案，從而推動市場成長。
  • 此外，美國公司也正在不斷擴大研發力度，開發新產品。例如，2022 年 12 月，Google LLC 宣布發布一款新工具，讓用戶在 Google Sheets 中開發人工智慧模型。該工具名為 Simple ML，現已推出測試版。它作為 Google Sheets附加元件提供，可供用戶免費下載。

深度學習行業概況

深度學習市場由IBM、Google和微軟等幾家大公司組成，這些公司擁有豐富的產業經驗，尤其是在巨量資料/分析平台方面，因此市場較為分散。其他新參與企業也進入了市場，並成功增加了整個行業的深度學習用例數量。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 - 為了推進電訊業機器學習 (ML) 技術和人工智慧 (AI) 領域的發展，Telenor 和愛立信簽署了一份為期三年的合作合作備忘錄（Memorandum of Understanding），以探索和開發）簽署。 ，測試先進的人工智慧/機器學習解決方案，以在不影響行動網路連接品質的情況下提高能源效率。

2022 年 10 月，Zendesk Inc. 宣布發布智慧分類和智慧輔助，這是新的人工智慧解決方案，使企業能夠自動分類客戶支援請求並大規模存取有價值的資料。

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) 軟體領域的領導者 Rapid Miner。透過此次收購，Altair 期待加強其端到端資料分析 (DA) 產品組合。

其他福利

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

目錄

第1章簡介

  • 研究假設和市場定義
  • 調查範圍

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
    • 替代產品的威脅
    • 競爭公司之間的敵意強度
  • 產業相關人員分析
  • 評估 COVID-19感染疾病對深度學習市場的影響

第5章市場動態

  • 市場促進因素
    • 計算能力的提高與大規模非結構化資料的存在相結合
    • 持續努力將深度學習整合到消費群的解決方案中
    • 零售領域擴大使用深度學習來推動市場發展
  • 市場課題
    • 營運和基礎設施問題，例如硬體複雜性和對熟練勞動力的需求
  • 市場機會
  • 深度學習技術演進
  • 主要機器學習庫分析

第6章市場區隔

  • 提供
    • 硬體
    • 軟體和服務
  • 最終用戶產業
    • BFSI
    • 零售
    • 製造業
    • 衛生保健
    • 車
    • 通訊和媒體
    • 其他最終用戶產業
  • 目的
    • 影像識別
    • 訊號識別
    • 資訊處理
    • 其他用途
  • 地區
    • 北美洲
    • 歐洲
    • 亞太地區
    • 世界其他地區

第7章 競爭形勢

  • 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 24.73 billion in 2024, and is expected to reach USD 138.36 billion by 2029, growing at a CAGR of 41.10% during the forecast period (2024-2029).

Deep Learning - Market

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