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人工智能 DL/ML

市場調查報告書

商品編碼

1517578

自動機器學習 (AutoML) 市場規模 - 按產品、部署模式、企業規模、應用程式、最終用戶和預測，2024 年至 2032 年

Automated Machine Learning (AutoML) Market Size - By Offering, By Deployment Mode, By Enterprise Size, By Application, By End-User & Forecast, 2024 - 2032

出版日期: 2024年04月15日 | 出版商:

Global Market Insights Inc. | 英文 260 Pages | 商品交期: 2-3個工作天內

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簡介目錄

由於企業領導者之間的策略對話不斷增加，2024 年至 2032 年全球自動化機器學習 (AutoML) 市場的複合年成長率將超過 30%。這些合作結合了人工智慧、資料科學和雲端運算方面的專業知識，以創新和提供強大的 AutoML 解決方案。透過整合，領先公司正在透過自動化模型建置、特徵工程和超參數最佳化功能來增強其平台。

例如，2023 年 9 月，富士通有限公司與 Linux 基金會合作，在西班牙畢爾巴鄂舉行的 2023 年歐洲開源高峰會之前，正式推出了自動化機器學習和人工智慧公平技術作為開源軟體 (OSS)。這些名為 SapientML 和 Intersection Fairness 的措施旨在為使用者提供自動產生新機器學習模型程式碼並解決訓練資料偏差的工具。

這種連接正在加速人工智慧在醫療保健、金融和零售等行業的採用，而強大的資料分析對於這些行業至關重要。這些合作夥伴關係也擴大了市場範圍，支援根據客戶需求客製化解決方案，從新創公司到企業級組織。隨著競爭的加劇，AutoML 市場的聯盟支持預測分析和機器學習的創新，從而提高效率和可擴展性。最終，這些合作夥伴關係促進了技術進步，並使人工智慧驅動的見解變得容易獲得。

整體自動化機器學習 (AutoML) 行業規模根據產品、部署模式、企業規模、應用程式、最終用戶和區域進行分類。

從 2024 年到 2032 年，該服務領域的自動化機器學習 (AutoML) 市場收入將實現令人稱讚的複合年成長率。專業人員提供諮詢、修改和實施管理，為各行業創建客製化的 AutoML 解決方案。公司正在使用這些服務來加快原型設計、提高準確性並更好地將人工智慧整合到其營運中。隨著越來越重視資料驅動的決策，企業越來越依賴服務提供者來應對實施人工智慧的挑戰，確保靈活性和合規性。隨著對複雜人工智慧功能的需求增加，AutoML 市場服務領域不斷擴大。

從 2024 年到 2032 年，本地市場將資料成長。園區內的 AutoML 平台使企業能夠更好地控制其資料和工作流程，確保關鍵資訊保留在其基礎設施內。這種部署模式對醫療保健、金融和政府等行業也很有吸引力，這些行業嚴格的規則很重要。透過採用本地 AutoML 解決方案，企業可以提高營運效率、減少資料處理時間並遵守本地和國際資料保護法規。雖然組織降低了利用人工智慧功能所需的風險，但對基於位置的 AutoML 解決方案的需求仍在不斷成長。

亞太地區自動化機器學習 (AutoML) 市場從 2024 年到 2032 年將呈現顯著的複合年成長率。製造、醫療保健和零售業使用 AutoML 來簡化營運、改善決策流程並獲得競爭優勢。 AutoML 解決方案著重可擴充性和效率，可滿足動態市場環境中的業務需求。政府促進人工智慧創新的政策支持對人工智慧能力的合作和投資，進一步刺激市場成長。隨著亞太地區經濟體接受人工智慧驅動的洞察，對 AutoML 解決方案的需求不斷擴大，塑造新產業的未來。

產業生態系統分析
供應商格局
- 技術提供者
- 服務供應商
- 平台提供者
- 終端用戶
利潤率分析
技術與創新格局
專利分析
重要新聞和舉措
監管環境
衝擊力
- 成長動力
  - 對人工智慧解決方案的需求不斷成長
  - 缺乏熟練的資料科學家
  - 與雲端服務整合的增加
  - 客製化選項和靈活性的提高
- 產業陷阱與挑戰
  - 引起對資料隱私的擔憂
  - 資料和模型的複雜性
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 2021 - 2032 年發行

主要趨勢
解決方案
服務
- 諮詢
- 一體化
- 部署

第 6 章：市場估計與預測：依部署模式，2021 - 2032 年

主要趨勢
雲
本地

第 7 章：市場估計與預測：依企業規模，2021 - 2032

主要趨勢
中小企業
- 解決方案
- 服務
  - 諮詢
  - 一體化
  - 部署
大型企業
- 解決方案
- 服務
  - 諮詢
  - 一體化
  - 部署

第 8 章：市場估計與預測：依應用分類，2021 - 2032

主要趨勢
資料處理
特徵工程
選型
超參數最佳化和調整
模特兒合奏
其他

第 9 章：市場估計與預測：按最終用戶分類，2021 - 2032 年

主要趨勢
資訊科技與電信
BFSI
零售
汽車
媒體與娛樂
其他

第 10 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
- 義大利
- 俄羅斯
- 西班牙
- 歐洲其他地區
亞太地區
- 中國
- 日本
- 印度
- 韓國
- 澳洲
- 東南亞
- 亞太地區其他地區
拉丁美洲
- 巴西
- 墨西哥
- 阿根廷
- 拉丁美洲其他地區
MEA
- 阿拉伯聯合大公國
- 南非
- 沙烏地阿拉伯
- MEA 的其餘部分

第 11 章：公司簡介

Alphabet Inc.
Alteryx
Amazon Web Services, Inc.
Auger.AI
BigML
DarwinAI
Databricks AutoML
Dataiku
DataRobot MLOps
DataRobot Paxata
DataRobot, Inc.
DotData
Feature Labs
H2O.ai
HPE Haven OnDemand
IBM Corporation
KNIME
Microsoft
RapidMiner Auto Model
TIBCO Software Inc.

簡介目錄

Product Code: 9033

Global Automated Machine Learning (AutoML) Market will observe a CAGR of over 30% from 2024 to 2032 due to rising strategic conversations between business leaders. These collaborations combine expertise in AI, data science, and cloud computing to innovate and deliver robust AutoML solutions. Through integration, leading companies are enhancing their platforms with automated model building, feature engineering, and hyperparameter optimization capabilities.

For instance, in September 2023, Fujitsu Limited, in collaboration with the Linux Foundation, officially launched its automated machine learning and AI fairness technologies as open-source software (OSS) ahead of the Open Source Summit Europe 2023 in Bilbao, Spain. These initiatives, named SapientML and Intersectional Fairness,aim to provide users with tools that automatically generate code for new machine learning models and address biases in training data.

This connectivity is accelerating the adoption of AI in industries such as healthcare, finance, and retail, where robust data analytics are essential. These partnerships also expand market reach, enabling solutions tailored to the needs of customers, from start-ups to enterprise-level organizations. As competition intensifies, alliances in the AutoML market support innovation in predictive analytics and machine learning, improving efficiency and scalability. Ultimately, these partnerships spur technological advancements and make AI-driven insights accessible.

Overall Automated Machine Learning (AutoML) Industry size is classified based on offering, deployment mode, enterprise size, application, end-user, and region.

The Automated Machine Learning (AutoML) market revenue from the service segment will register a commendable CAGR from 2024 to 2032. The services are popular due to the need for basic skills in implementing and managing machine learning models. Professionals provide consulting, modification, and implementation management to create customized AutoML solutions for various industries. Companies are using these services to speed up prototyping, improve accuracy, and better integrate AI into their operations. With an increasing emphasis on data-driven decision-making, companies increasingly rely on service providers to navigate the challenges of implementing AI, ensuring flexibility and compliance. As demand for sophisticated AI capabilities increases, the AutoML market service segment continues to expand.

The on-premises segment will witness an appreciable growth from 2024 to 2032. The demand for on-premises solutions addresses an organization's prioritization of data privacy, security, and compliance. AutoML platforms on campus give enterprises greater control over their data and workflow, ensuring critical information stays within their infrastructure. This deployment model is also attractive to industries such as healthcare, finance, and government, where strict rules are important. By adopting on-premise AutoML solutions, businesses increase operational efficiencies, reduce data processing time, and comply with local and international data protection regulations. While organizations reduce the risk required to leverage AI capabilities, the demand for location-based AutoML solutions continues to grow.

Asia Pacific automated machine learning (AutoML) market will exhibit a notable CAGR from 2024 to 2032. The demand in the region is driven by rapid digital transformation and increasing adoption of AI technologies. Businesses in manufacturing, healthcare, and retail use AutoML to streamline operations, improve decision-making processes, and gain competitive advantage. With a focus on scalability and efficiency, AutoML solutions meet business needs in a dynamic market environment. Governments' policies to promote AI innovation support partnerships and investments in AI capabilities, further stimulating market growth. As Asia Pacific economies embrace AI-powered insights, the demand for AutoML solutions continues to expand, shaping the future of new industries.

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 degree synopsis, 2021 - 2032

Chapter 3 Industry Insights

3.1 Industry ecosystem analysis
3.2 Supplier landscape
- 3.2.1 Technology providers
- 3.2.2 Service providers
- 3.2.3 Platform providers
- 3.2.4 End users
3.3 Profit margin analysis
3.4 Technology & innovation landscape
3.5 Patent analysis
3.6 Key news & initiatives
3.7 Regulatory landscape
3.8 Impact forces
- 3.8.1 Growth drivers
  - 3.8.1.1 Growing demand for ai solutions
  - 3.8.1.2 Shortage of skilled data scientists
  - 3.8.1.3 Rise in the integration with cloud services
  - 3.8.1.4 Rise in the customization options and flexibility
- 3.8.2 Industry pitfalls & challenges
  - 3.8.2.1 Raising concerns about data privacy
  - 3.8.2.2 Complexity of data and models
3.9 Growth potential analysis
3.10 Porter's analysis
3.11 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 Offering 2021 - 2032 ($Mn)

5.1 Key trends
5.2 Solution
5.3 Service
- 5.3.1 Consulting
- 5.3.2 Integration
- 5.3.3 Deployment

Chapter 6 Market Estimates & Forecast, By Deployment Mode, 2021 - 2032 ($Mn)

6.1 Key trends
6.2 Cloud
6.3 On-premises

Chapter 7 Market Estimates & Forecast, By Enterprise size, 2021 - 2032 ($Mn)

7.1 Key trends
7.2 SMEs
- 7.2.1 Solution
- 7.2.2 Service
  - 7.2.2.1 Consulting
  - 7.2.2.2 Integration
  - 7.2.2.3 Deployment
7.3 Large enterprises
- 7.3.1 Solution
- 7.3.2 Service
  - 7.3.2.1 Consulting
  - 7.3.2.2 Integration
  - 7.3.2.3 Deployment

Chapter 8 Market Estimates & Forecast, By Application, 2021 - 2032 ($Mn)

8.1 Key trends
8.2 Data processing
8.3 Feature engineering
8.4 Model selection
8.5 Hyperparameter optimization & tuning
8.6 Model ensemble
8.7 Others

Chapter 9 Market Estimates & Forecast, By End-User, 2021 - 2032 ($Mn)

9.1 Key trends
9.2 IT & telecommunications
9.3 BFSI
9.4 Retail
9.5 Automotive
9.6 Media & entertainment
9.7 Others

Chapter 10 Market Estimates & Forecast, By Region, 2021 - 2032 ($Mn)

10.1 Key trends
10.2 North America
- 10.2.1 U.S.
- 10.2.2 Canada
10.3 Europe
- 10.3.1 UK
- 10.3.2 Germany
- 10.3.3 France
- 10.3.4 Italy
- 10.3.5 Russia
- 10.3.6 Spain
- 10.3.7 Rest of Europe
10.4 Asia Pacific
- 10.4.1 China
- 10.4.2 Japan
- 10.4.3 India
- 10.4.4 South Korea
- 10.4.5 Australia
- 10.4.6 Southeast Asia
- 10.4.7 Rest of Asia Pacific
10.5 Latin America
- 10.5.1 Brazil
- 10.5.2 Mexico
- 10.5.3 Argentina
- 10.5.4 Rest of Latin America
10.6 MEA
- 10.6.1 UAE
- 10.6.2 South Africa
- 10.6.3 Saudi Arabia
- 10.6.4 Rest of MEA