查看購物車
  • Simplified Chinese
  • English
  • Japanese
  • Korean
封面
市場調查報告書
商品編碼
1692109

自動化機器學習：市場佔有率分析、產業趨勢與統計、成長預測（2025-2030 年）

Automated Machine Learning - Market Share Analysis, Industry Trends & Statistics, Growth Forecasts (2025 - 2030)
出版日期: | 出版商: Mordor Intelligence | 英文 119 Pages | 商品交期: 2-3個工作天內

價格

※ 本網頁內容可能與最新版本有所差異。詳細情況請與我們聯繫。

簡介目錄

預計 2025 年自動機器學習市場價值為 25.9 億美元，到 2030 年將達到 159.8 億美元，預測期內（2025-2030 年）的複合年成長率為 43.9%。

自動機器學習-市場-IMG1

機器學習 (ML) 是人工智慧 (AI) 的一個分支，其中學習演算法使用統計方法進行分類和預測，揭示資料探勘計劃中的關鍵見解。這些見解推動應用和業務決策，並理想地影響關鍵成長指標。這些解決方案圍繞著演算法、模型和計算複雜性，因此需要由熟練的專家來開發。

主要亮點

  • 機器學習（ML）已成為一個必不可少的組成部分。然而，建立高效能機器學習應用程式需要高度專業化的資料科學家和領域專家。自動化機器學習 (AutoML) 旨在透過讓領域專家自動建立機器學習應用程式來減少對資料科學家的需求，而無需大量的統計或機器學習知識。
  • 物聯網、自動化和雲端基礎的服務的日益普及正在推動市場投資的增加。該解決方案使中小型企業和大型企業能夠外包他們需要的一切，以提高資料品質、安全性、安全性和機器學習應對力，從而避免僱用資料科學資源的成本和挑戰。該服務還得到了 Calligo 數據洞察平台的支持，該平台專為機器學習工作負載而建置。例如，2024 年 1 月，Google Cloud 和 Hugging Face 宣佈建立策略夥伴關係，以加速生成式 AI 和 ML 開發。此次合作將使開發者能夠將 Google Cloud 的基礎架構用於所有 Hugging Face 服務，從而允許 Hugging Face 模式在 Google Cloud 上進行訓練和使用。
  • 一些公司，例如 Facebook 和 Google，正在轉向 AutoML 來自動化其內部流程，尤其是 ML 模型的建立。 Asimo 是 Facebook 的 AutoML 開發人員，可自動對目前模型進行改進。谷歌還發布了 AutoML 工具，該工具可以自動尋找最佳模型和設計機器學習演算法的過程。谷歌宣布了「Cloud AutoML」。 「Cloud AutoML」是一款產品，可協助缺乏機器學習（ML）專業知識的公司建立高品質、自訂的人工智慧（AI）模型，為 Google 的產品和服務提供支援。 Cloud AutoML 使企業和開發人員能夠根據他們的使用案例訓練自訂視覺模型。每家公司的這些創新都將推動市場的發展。
  • 受醫療保健領域應用和研究日益增多的推動，AutoML 市場預計將實現顯著成長。隨著 AutoML 徹底改變患者護理和醫學研究，針對醫療保健挑戰的 AI主導解決方案的需求正在激增。 AutoML 可自動執行模型選擇和特徵工程等複雜的機器學習任務，簡化疾病診斷、治療最佳化和藥物發現的預測模型的開發。
  • 機器學習（ML）在許多應用中變得越來越普遍，但為了充分支援這種成長，我們需要更多的 ML 專家。自動化機器學習（AutoML）的目標是讓機器學習更容易實現。因此，專家應該能夠部署更多的機器學習系統，而且 AutoML 可能比直接使用 ML 所需的專業知識更少。然而，該技術的採用尚未深入，這限制了市場的成長。
  • 自從新冠肺炎疫情以來，隨著企業利用智慧解決方案實現業務流程自動化，我們看到人工智慧的採用增加。預計這一趨勢將在未來幾年持續下去，進一步推動人工智慧在組織流程中的應用。

自動化機器學習市場趨勢

BFSI 部門推動市場成長

  • 銀行、金融服務和保險 (BFSI) 行業擴大採用 AI 和 ML 技術來提高業務效率並增強消費者體驗。隨著資料變得越來越重要，機器學習 BFSI 應用程式的需求量很大。自動化機器學習可以利用大量資料、經濟的處理能力和經濟的儲存產生準確、快速的結果。
  • 此外，機器學習 (ML) 解決方案使金融公司能夠透過智慧流程自動化來自動執行重複業務，透過聊天機器人提高企業生產力，實現管理流程自動化以及員工培訓遊戲化，從而取代手動任務。機器學習有望用於實現財務流程的自動化。
  • 疫情爆發後，金融機構更專注於透過數位管道接觸並幫助客戶。如今，金融領域出現了一系列數位解決方案，包括聊天機器人、開戶和管理支援以及技術援助，Posh.Tech、Spixii 等公司現在提供智慧聊天機器人，旨在為銀行提供面向客戶的基本功能。
  • HDFC 銀行正在使用由班加羅爾 Senseforth AI Research 開發的基於 AI 的聊天機器人「Eva」。自今年 3 月推出以來，Eva（電子虛擬助理的縮寫）已經回覆了超過 270 萬個客戶諮詢，與超過 53 萬名獨立用戶進行了互動，並進行了 120 萬次對話。德意志銀行宣布與 NVIDIA 建立多年創新夥伴關係，以加速人工智慧 (AI) 和機器學習 (ML) 在金融領域的應用。
  • 銀行面臨越來越大的風險管理壓力和更嚴格的管治和監管要求，它們必須改善服務產品以更好地服務客戶。銀行詐騙案件的增加預計將推動人工智慧和機器學習的採用。一些金融科技品牌擴大在多個管道的各種應用中使用人工智慧和機器學習，以利用可用的客戶資料並預測客戶需求如何變化，哪些詐騙活動最有可能襲擊系統，哪些服務會有益，等等。
  • 23會計年度，印度儲備銀行（RBI）報告稱，印度全國發生了超過13,000起銀行詐騙案件，與上年度相比有所增加。這扭轉了過去十年的趨勢。銀行詐騙的增加可能會刺激進一步的市場需求。

北美佔據主要市場佔有率

  • 北美預計將佔據大部分市場佔有率，這得益於其強大的創新生態系統，該生態系統由聯邦政府對先進技術的戰略投資推動，並輔以來自全球各地有遠見的科學家和企業家，以及推動自動機器學習（AutoML）發展的認可研究機構。
  • 政府，包括州和地方政府，處理大量以前以紙本形式儲存並手動處理的公民資料。但隨著人工智慧（AI）和機器學習技術提供更快、更準確的資料收集和處理方法，政府可以專注於更複雜、更長期的社會和文化問題。此外，協作式機器學習的商業應用日益增加預計將推動對 AutoML 的需求。
  • 據加拿大政府稱，人工智慧（AI）技術有望增強加拿大政府向公民提供服務的方式。英國政府在審查人工智慧在政府計畫和服務中的使用時，確保有明確的價值觀、道德觀和規則指南。
  • 當美國試圖確立 AutoML 主導地位時，加拿大也在為此類發展做準備。例如，2023 年 4 月，ePayPolicy 宣布推出 Payables Connect，這是其保險支付和對帳產品套件的新功能。這將利用 ePay 現有的整合和機器學習技術，完全實現支付匹配、設計和支付的自動化。
  • 儘管加拿大仍處於各行業採用自動化機器學習的早期階段，但預計有幾個因素將推動市場成長，包括金融領域對自動化的需求日益成長以及學生對教育的興趣日益濃厚。
  • 該地區的 AutoML 市場正被雲端運算所改變。無伺服器運算使創作者能夠快速推出和運行 ML 應用程式。例如，根據AWS的數據，2023年10月美國雲端處理基礎設施支出將超過1,080億美元。
  • 此外，許多大大小小的組織都在從傳統商業模式轉型為數位商業模式。這種轉變催生了混合雲市場，因為它具有降低整體擁有成本（TCO）、提高安全性、靈活性和敏捷性等優勢。 IBM 表示，89% 的 IT 領導者希望將業務關鍵型工作負載轉移到雲端，這一切都源自於數位化的提升。此類雲端解決方案的擴展可能會進一步推動該地區的市場成長。

自動機器學習行業概覽

全球自動機器學習市場呈現中度分散化，大量參與者滿足市場需求。競爭是由新進入者的湧入推動的，促使現有進入者制定策略來擴大基本客群。這種動態情勢也刺激了技術創新，現有市場參與者紛紛努力開發尖端產品。著名的市場領導包括 Datarobot Inc.、Amazon Web Services Inc.、dotData Inc.、IBM Corporation 和 Dataiku。

  • 2024 年 2 月領先的技術服務和顧問公司 Wipro Limited 宣布推出Wipro Enterprise 人工智慧 (AI) Ready 平台。 Wipro Enterprise AI-Ready 平台由 IBM Watsonx AI 和資料平台提供支持，其中包括 watsonx.data、watsonx.ai 和 watsonx.ai。管治和 AI 助理為客戶提供可互通的服務，加速 AI 的採用。這項獨特的服務為業務提供了涵蓋工具、大型語言模型 (LLM)、簡化流程和強大管治的功能。它也為基於 watsonx.data 和 AI 的未來企業分析解決方案奠定了基礎。
  • 2024 年 5 月，Snapchat 推出了一系列尖端的擴增實境(AR) 和機器學習 (ML) 工具，旨在幫助品牌和廣告商為使用者創造互動體驗。該公司正在投資自動化和機器學習，以便品牌更快、更輕鬆地創建 AR 試穿資產。
  • 2023 年 9 月 富士通有限公司和 Linux 基金會在 2023 年 9 月於西班牙畢爾巴鄂舉行的 2023 年歐洲開放原始碼高峰會之前宣布將富士通的自動機器學習和 AI 公平性技術作為開放原始碼軟體(OSS)。這兩個計劃預計將為用戶提供軟體來自動生成自己的機器學習模型的程式碼，以及解決訓練資料中潛在偏差的技術。

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 市場動態

  • 市場促進因素
    • 對高效詐欺偵測解決方案的需求日益增加
    • 對智慧業務流程的需求不斷增加
  • 市場限制
    • 自動化機器學習工具的採用緩慢
  • 產業價值鏈分析
  • 產業吸引力-波特五力分析
    • 新進入者的威脅
    • 買家的議價能力
    • 供應商的議價能力
    • 替代品的威脅
    • 競爭對手之間的競爭強度
  • 主要宏觀經濟趨勢將如何影響市場

第5章 市場區隔

  • 按解決方案
    • 獨立或本地
    • 雲
  • 按自動化類型
    • 資料處理
    • 特徵工程
    • 造型
    • 視覺化
  • 按最終用戶
    • BFSI
    • 零售與電子商務
    • 衛生保健
    • 製造業
    • 其他最終用戶
  • 按地區
    • 北美洲
      • 美國
      • 加拿大
    • 歐洲
      • 英國
      • 德國
      • 法國
      • 其他歐洲國家
    • 亞太地區
      • 中國
      • 日本
      • 韓國
      • 其他亞太地區
    • 世界其他地區

第6章 競爭格局

  • 公司簡介
    • DataRobot Inc.
    • Amazon web services Inc.
    • dotData Inc.
    • IBM Corporation
    • Dataiku
    • SAS Institute Inc.
    • Microsoft Corporation
    • Google LLC（Alphabet Inc.）
    • H2O.ai
    • Aible Inc.

第7章投資分析

第 8 章：市場的未來

簡介目錄
Product Code: 90609

The Automated Machine Learning Market size is estimated at USD 2.59 billion in 2025, and is expected to reach USD 15.98 billion by 2030, at a CAGR of 43.9% during the forecast period (2025-2030).

Automated Machine Learning - Market - IMG1

Machine learning (ML) is a subfield of artificial intelligence (AI) that enables training algorithms to make classifications or predictions through statistical methods, uncovering critical insights within data mining projects. These insights drive decision-making within applications and businesses, ideally impacting key growth metrics. Skilled professionals must develop these solutions since they revolve around algorithms, models, and computational complexity.

Key Highlights

  • Machine learning (ML) has become an essential component. On the other hand, building high-performance machine-learning applications necessitates highly specialized data scientists and domain experts. Automated machine learning (AutoML) aims to decrease data scientists' needs by allowing domain experts to automatically construct machine learning applications without considerable statistics and machine learning knowledge.
  • Due to the increasing adoption of IoT, automation, and cloud-based services, investment in the market has been rising. The solution allows SMEs and enterprises to outsource everything needed to improve data quality, security, safety, and readiness for machine learning and avoid the cost and challenges of employing a data science resource. This service is also supported by Calligo's Data Insights Platform, which is purpose-built for machine learning workloads. For instance, in January 2024, Google Cloud and Hugging Face Announced a Strategic Partnership to Accelerate Generative AI and ML Development. This collaboration will allow developers to utilize Google Cloud's infrastructure for all Hugging Face services, enabling training and serving of Hugging Face models on Google Cloud.
  • Some firms have shifted to AutoML to automate internal procedures, particularly the creation of ML models, such as Facebook and Google. Asimo is Facebook's AutoML developer, which automatically generates improved versions of current models. Google also released AutoML tools to automate the process of discovering optimization models and designing machine learning algorithms. Google launched "Cloud AutoML," a product that allows businesses with limited Machine Learning (ML) expertise to build high-quality, custom artificial intelligence (AI) models to enhance Google's products and services. "Cloud AutoML" lets businesses and developers train custom vision models for their use cases. Such innovations by the companies will drive the market.
  • The AutoML market is expected to experience significant growth, driven by rising applications and research in the medical field. As AutoML revolutionizes patient care and medical research, there is a surge in demand for AI-driven solutions tailored to healthcare challenges. AutoML can automate complex machine learning tasks, such as model selection and feature engineering, to streamline the development of predictive models for illness diagnosis, treatment optimization, and drug discovery.
  • Machine learning (ML) is increasingly used in many applications, but there needs to be more machine learning experts to support this growth adequately. With automated machine learning (AutoML), the purpose is to make machine learning more accessible. Therefore, experts should be able to deploy more machine learning systems, and less expertise would be required to work with AutoML than when working with ML directly. However, the adoption of technology still needs to be deeper, restraining the market's growth.
  • The adoption of AI witnessed an increase post-COVID-19 as companies leveraged intelligent solutions for automating their business processes. This trend is anticipated to continue over the coming years, further driving the adoption of AI in organizational processes.

Automated Machine Learning Market Trends

The BFSI Segment is Driving Market Growth

  • AI and ML technologies are increasingly adopted in the banking, financial services, and insurance (BFSI) industry to enhance operational efficiency and improve the consumer experience. As data gains more attention, the demand for machine learning BFSI applications grows. Automated machine learning can produce accurate and rapid results with enormous data, affordable processing power, and economical storage.
  • Machine learning (ML)-powered solutions also enable finance firms to replace manual labor by automating repetitive operations through intelligent process automation, increasing corporate productivity for chatbots, paperwork automation, and employee training gamification, among others. Machine learning is expected to be used to automate financial processes.
  • After the pandemic, financial institutions showed increased interest in reaching and assisting customers through digital channels. Various digital solutions, including chatbots, account opening and management support, and technical assistance, witnessed a surge in adoption within the finance sector, especially in fintech corporations like Posh. Tech, Spixii, and numerous others now provide intelligent chatbots designed to facilitate essential customer-facing functions for banks.
  • HDFC Bank uses an AI-based chatbot, "Eva," built by Bengaluru-based Senseforth AI Research. Since its launch in March this year, Eva (which stands for Electronic Virtual Assistant) has addressed over 2.7 million client queries, interacted with over 530,000 unique users, and held 1.2 million conversations. Deutsche Bank announced a multi-year innovation partnership with NVIDIA to accelerate the use of artificial intelligence (AI) and machine learning (ML) in the finance sector.
  • Banks must improve their services to offer better customer service with the rising pressure in managing risk and increasing governance and regulatory requirements. The rising number of bank fraud cases is expected to increase the adoption of AI and ML. Some fintech brands have been increasingly using AI and ML in different applications across multiple channels to leverage available client data and predict how customers' needs are evolving, which fraudulent activities have the highest possibility to attack a system, and what services will prove beneficial, among others.
  • In FY 2023, the Reserve Bank of India (RBI) reported more than 13 thousand bank fraud cases across India, an increase compared to the previous year. It turned around the previous decade's trend. Such increases in bank fraud may further generate market demand.

North America to Hold a Significant Market Share

  • North America is expected to hold a substantial share of the market owing to the robust innovation ecosystem, fueled by strategic federal investments into advanced technology, complemented by the existence of visionary scientists and entrepreneurs coming together from across the world and recognized research institutions, driving the development of automated machine learning (AutoML).
  • Various governments, including state and local governments, handle enormous quantities of citizen data, which used to be stored on paper and processed manually. However, as artificial intelligence (AI) and machine learning technologies provide faster and more accurate data-gathering and processing methods, governments can focus on more complex and long-term social and cultural issues. Further, an increase in commercial applications for federated ML is expected to drive the demand for AutoML.
  • According to the Government of Canada, artificial intelligence (AI) technologies promise to enhance how the Canadian government serves its citizens. As the government investigates the usage of artificial intelligence in government programs and services, it ensures that clear values, ethics, and rules guide it.
  • While the United States is trying to establish AutoML supremacy, Canada is also gearing up for such developments. For instance, in April 2023, ePayPolicy launched Payables Connect, the latest addition to its insurance payment and reconciliation products suite. It leverages ePay's existing integration and machine learning technology to automate the reconciliation, design, and payment of due payables completely.
  • Though Canada is still in the initial phase of deploying automated machine learning across various industries, some factors, including the rising need to automate the finance sector and the emerging educational interest among students, are expected to drive market growth.
  • The region's AutoML market is changing due to the cloud; serverless computing allows creators to get ML applications up and running quickly. For instance, in October 2023, according to AWS, US cloud computing infrastructure investment exceeded USD 108 billion.
  • Moreover, many organizations of different sizes are transforming from traditional to digital modes of business. This transformation creates a hybrid cloud market because of the benefits, like reduced total cost of ownership (TCO), high security, flexibility, and agility. IBM stated that 89% of IT leaders are expected to move business-critical workloads to the cloud, and the growth in digitization drives all. Such expansion in cloud solutions may further propel the market's growth in the region.

Automated Machine Learning Industry Overview

The global automated machine learning market exhibits moderate fragmentation, with numerous players meeting market demands. The competition is driven by the influx of new entrants, prompting existing participants to devise strategies for expanding their customer base. This dynamic landscape also spurs innovation as existing market players strive to develop cutting-edge products. Notable market leaders include Datarobot Inc., Amazon Web Services Inc., dotData Inc., IBM Corporation, and Dataiku.

  • February 2024: Wipro Limited, a significant technology services and consulting corporation, announced the launch of Wipro Enterprise Artificial Intelligence (AI)-Ready Platform, a new service allowing clients to create enterprise-level, fully integrated, and customized AI environments. The Wipro Enterprise AI-Ready Platform leverages the IBM Watsonx AI and data platform, including watsonx.data, watsonx.ai, and watsonx. Governance and AI assistants offer clients an interoperable service that accelerates AI adoption. This unique service enhances operations with capabilities spanning tools, large language models (LLMs), streamlined processes, and strong governance. It also lays the foundation for future enterprise analytic solutions to be built on watsonx.data and AI.
  • May 2024: Snapchat announced a series of the latest augmented reality (AR) and machine learning (ML) tools developed to help brands and advertisers provide users with interactive experiences. The company had been investing in automation and ML to make it faster and easier for brands to create AR try-on assets.
  • September 2023: Fujitsu Limited and the Linux Foundation announced the launch of Fujitsu's automated machine learning and AI fairness technologies as open-source software (OSS) ahead of the "Open Source Summit Europe 2023," running in Bilbao, Spain, from September 2023. The two projects were expected to offer users access to software that automatically generates code for unique machine-learning models and a technology that addresses latent biases in training data.

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 DYNAMICS

  • 4.1 Market Drivers
    • 4.1.1 Increasing Demand for Efficient Fraud Detection Solutions
    • 4.1.2 Growing Demand for Intelligent Business Processes
  • 4.2 Market Restraints
    • 4.2.1 Slow Adoption of Automated Machine Learning Tools
  • 4.3 Industry Value Chain Analysis
  • 4.4 Industry Attractiveness - Porter's Five Forces Analysis
    • 4.4.1 Threat of New Entrants
    • 4.4.2 Bargaining Power of Buyers
    • 4.4.3 Bargaining Power of Suppliers
    • 4.4.4 Threat of Substitute Products
    • 4.4.5 Intensity of Competitive Rivalry
  • 4.5 Impact of Key Macroeconomic Trends on the Market

5 MARKET SEGMENTATION

  • 5.1 By Solution
    • 5.1.1 Standalone or On-Premise
    • 5.1.2 Cloud
  • 5.2 By Automation Type
    • 5.2.1 Data Processing
    • 5.2.2 Feature Engineering
    • 5.2.3 Modeling
    • 5.2.4 Visualization
  • 5.3 By End User
    • 5.3.1 BFSI
    • 5.3.2 Retail and E-Commerce
    • 5.3.3 Healthcare
    • 5.3.4 Manufacturing
    • 5.3.5 Other End Users
  • 5.4 By Geography
    • 5.4.1 North America
      • 5.4.1.1 United States
      • 5.4.1.2 Canada
    • 5.4.2 Europe
      • 5.4.2.1 United Kingdom
      • 5.4.2.2 Germany
      • 5.4.2.3 France
      • 5.4.2.4 Rest of Europe
    • 5.4.3 Asia-Pacific
      • 5.4.3.1 China
      • 5.4.3.2 Japan
      • 5.4.3.3 South Korea
      • 5.4.3.4 Rest of Asia-Pacific
    • 5.4.4 Rest of the World

6 COMPETITIVE LANDSCAPE

  • 6.1 Company Profiles
    • 6.1.1 DataRobot Inc.
    • 6.1.2 Amazon web services Inc.
    • 6.1.3 dotData Inc.
    • 6.1.4 IBM Corporation
    • 6.1.5 Dataiku
    • 6.1.6 SAS Institute Inc.
    • 6.1.7 Microsoft Corporation
    • 6.1.8 Google LLC (Alphabet Inc.)
    • 6.1.9 H2O.ai
    • 6.1.10 Aible Inc.

7 INVESTMENT ANALYSIS

8 FUTURE OF THE MARKET