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市場調查報告書
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1445737

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（2024 - 2029）

Automated Machine Learning - Market Share Analysis, Industry Trends & Statistics, Growth Forecasts (2024 - 2029)
出版日期: | 出版商: Mordor Intelligence | 英文 119 Pages | 商品交期: 2-3個工作天內

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簡介目錄

自動化機器學習市場規模估計到2024年將達到 18 億美元，預計到2029年將達到 111.2 億美元，在預測期內（2024-2029年）CAGR為 43.90%。

自動化機器學習 - 市場

主要亮點

  • 機器學習（ML）是人工智慧（AI）的一個子領域，它使訓練演算法能夠透過統計方法進行分類或預測，揭示資料探勘項目中的關鍵見解。這些見解推動應用程式和業務內的決策，理想情況下會影響關鍵成長指標。由於它圍繞著演算法、模型和計算複雜性，因此必須由熟練的專業人員開發這些解決方案。
  • 機器學習（ML）已成為業務許多部分的重要組成部分。另一方面，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）目的是透過允許領域專家在沒有大量統計和機器學習知識的情況下自動建立機器學習應用程式來減少資料科學家的需求。
  • 由於資料科學和人工智慧的改進，自動化機器學習的性能得到了提高。公司認知到這項技術的潛力，因此其採用率在預測期內可能會上升。公司以訂閱方式銷售自動化機器學習解決方案，使客戶更容易使用該技術。此外，它還提供按需付費的靈活性。
  • 機器學習（ML）擴大應用於許多應用中，但機器學習專家不足，無法充分支援這種成長。自動化機器學習（AutoML）的目標是讓機器學習更容易使用。因此，專家應該能夠部署更多的機器學習系統，並且與直接使用 ML 相比，使用 AutoML 所需的專業知識更少。然而，技術採用仍然膚淺，限制了市場的成長。
  • 在 COVID-19 大流行之後，隨著公司轉向利用智慧解決方案來實現業務流程自動化，人工智慧的採用日益增加。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採用。

自動機器學習（AutoML）市場趨勢

BFSI 將成為最大的最終用戶產業

  • 近年來，人工智慧（AI）和機器技術擴大應用於銀行、金融服務和保險（BFSI）行業，以提高營運效率並改善消費者體驗。隨著資料獲得更多關注，對機器學習 BFSI 應用程式的需求不斷成長。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來產生準確、快速的結果。
  • 機器學習（ML）支援的解決方案還使金融公司能夠透過智慧流程自動化實現重複操作的自動化，取代體力勞動，提高企業生產力。在預測期內，範例包括聊天機器人、文書工作自動化和員工培訓遊戲化。機器學習預計將用於自動化財務流程。
  • COVID-19 大流行後，金融機構對透過數位管道接觸並幫助客戶表現出越來越大的興趣。各種數位解決方案，包括聊天機器人、開戶和管理支援以及技術援助，在金融領域的採用激增。值得注意的是，Posh.Tech、Spixii 等金融科技公司現在提供智慧聊天機器人，目的是促進銀行面向客戶的基本功能
  • 隨著風險管理壓力的增加以及治理和監管要求的提高，銀行必須加強服務，以提供更好的客戶服務。銀行詐欺案件數量的增加預計將增加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程式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的需求如何變化，哪些詐欺活動最有可能攻擊系統，哪些服務將被證明是有益的等等。

北美將佔據重要市場佔有率

  • 由於強大的創新生態系統、聯邦對先進技術的戰略投資的推動，再加上來自世界各地的有遠見的科學家和企業家以及公認的研究機構的存在，美國預計將在市場上佔有相當大的佔有率.推動了自動化機器學習（AutoML）的發展。
  • 包括州和地方政府在內的各個政府處理大量的公民資料，這些資料以前儲存在紙本文件上並進行手動處理。然而，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，政府可以致力於更複雜和長期的社會和文化問題。此外，federatedML 商業應用的增加預計將進一步推動對 AutoML 的需求。
  • 加拿大政府表示，人工智慧（AI）技術對於改善加拿大政府為其公民提供服務的方式具有巨大前景。當政府調查人工智慧在政府計畫和服務中的使用時，它確保明確的價值觀、道德和規則來指導它。
  • 在美國試圖建立 AutoML 霸主地位的同時，加拿大也為此類發展做準備。例如，2023年 4月，ePayPolicy 推出了 Payables Connect，這是其保險支付和對帳產品套件的新成員。它利用 ePay 現有的整合和機器學習技術來完全自動化到期應付帳款的對帳、創建和支付。
  • 儘管加拿大仍處於在各個行業部署自動化機器學習的初始階段，但一些因素，包括金融部門自動化需求的不斷成長以及學生對教育興趣的興起，預計將推動市場成長。
  • 該地區的 AutoML 市場因雲端而發生變化，無伺服器運算使創建者能夠快速啟動並運行 ML 應用程式。

自動機器學習（AutoML）產業概述

全球自動化機器學習市場表現出適度的分散性，眾多參與者滿足市場需求。新進入者的湧入推動了競爭的加劇，促使現有參與者制定擴大客戶群的策略。隨著現有市場參與者努力開發尖端產品，這種動態格局也刺激了創新。著名的行業領導者包括 Datarobot Inc.、Amazon Web Services Inc.、dotData Inc.、IBM Corporation 和 Dataiku。

2023年 8月，DataRobot 推出了新的生成人工智慧（AI）產品，其中包括平台功能和應用人工智慧服務，目的是透過產生人工智慧來加快從概念到價值的旅程。

2023年 8月，dotData Inc. 推出了新一代無程式碼 MLOps 平台 dotData Ops。該平台透過提供直覺的自助服務環境來幫助機器學習工程師高效部署和操作資料、功能和預測管道。

附加優惠：

  • Excel 格式的市場估算（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 研究方法

第3章 執行摘要

第4章 市場動態

  • 市場促進因素
    • 對高效詐欺檢測解決方案的需求不斷成長
    • 對智慧業務流程的需求不斷成長
  • 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
    • 替代產品的威脅
    • 競爭激烈程度
  • 評估 COVID-19 對市場的影響

第5章 市場細分

  • 依解決方案
    • 獨立或本地部署
    • 雲端
  • 按自動化類型
    • 資料處理
    • 特徵工程
    • 造型
    • 視覺化
  • 由最終用戶
    • BFSI
    • 零售與電子商務
    • 衛生保健
    • 製造業
    • 其他最終用戶
  • 按地理
    • 北美洲
      • 美國
      • 加拿大
    • 歐洲
      • 英國
      • 德國
      • 法國
      • 歐洲其他地區
    • 亞太
      • 中國
      • 日本
      • 韓國
      • 亞太其他地區
    • 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 1.8 billion in 2024, and is expected to reach USD 11.12 billion by 2029, growing at a CAGR of 43.90% during the forecast period (2024-2029).

Automated Machine Learning - Market

Key Highlights

  • Machine learning (ML) is a subfield of artificial intelligence (AI) that enables training algorithms to make classifications or predictions through statistical methods, uncovering key insights within data mining projects. These insights drive decision-making within applications and businesses, ideally impacting key growth metrics. Since it revolves around algorithms, models, and computational complexity, skilled professionals must develop these solutions.
  • Machine learning (ML) has become an essential component of many parts of the business. 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 knowledge of statistics and machine learning.
  • The performance of automated machine learning has advanced due to data science and artificial intelligence improvements. Companies recognize the potential of this technology, and hence its adoption rate is likely to rise over the forecast period. Companies are selling automated machine learning solutions on a subscription basis, making it easier for customers to use this technology. Furthermore, it offers flexibility on a pay-as-you-go basis.
  • Machine learning (ML) is increasingly used in many applications, but there are insufficient machine learning experts to support this growth adequately. With automated machine learning (AutoML), the aim is to make machine learning easier to use. Therefore, experts should be able to deploy more machine learning systems, and less expertise would be needed to work with AutoML than when working with ML directly. However, the technology adoption is still shallow, restraining the market's growth.
  • The adoption of AI is witnessing an increase after the COVID-19 pandemic as companies move towards leveraging intelligent solutions for automating their business processes. This trend is expected to continue over the coming years, further driving the adoption of AI in organizational processes.

Automated Machine Learning (AutoML) Market Trends

BFSI to be the Largest End-user Industry

  • In recent years, artificial intelligence (AI) and machine technologies have been increasingly adopted in the banking, financial services, and insurance (BFSI) industry to enhance operational efficiency and improve the consumer experience. As data gain 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. Over the predicted period, examples include chatbots, paperwork automation, and employee training gamification. Machine learning is expected to be used to automate financial processes.
  • Post-COVID-19 pandemic, financial institutions are showing a growing interest in reaching and assisting customers through digital channels. Various digital solutions, including chatbots, support for account opening and management, and technical assistance, have seen a surge in adoption within the financial sector. Notably, fintech companies like Posh.Tech, Spixii, and numerous others now provide intelligent chatbots designed to facilitate essential customer-facing functions for banks
  • Banks must enhance 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 various 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.

North America to Hold Significant Market Share

  • The United States is expected to hold a substantial share in the market owing to the strong 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, which has driven the development of automated machine learning (AutoML).
  • Various governments, including state and local governments, handle enormous quantities of citizen data, which had earlier been 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 federatedML is further expected to drive demand for AutoML.
  • According to the Government of Canada, artificial intelligence (AI) technologies hold great promise for enhancing how the Canadian government serves its citizens. As the government investigates the use of artificial intelligence in government programs and services, it ensures that clear values, ethics, and rules guide it.
  • While the US is trying to establish AutoML supremacy, Canada is also gearing up for such developments. For instance, in April 2023, ePayPolicy launched Payables Connect, the new addition to its suite of insurance payment and reconciliation products. It leverages ePay's existing integration and machine learning technology to completely automate the reconciliation, creation, and payment of due payables.
  • 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 financial sector and the emerging educational interest among students, are expected to drive market growth.
  • The region's AutoML marketplace is changing due to the cloud, and serverless computing allows creators to get ML applications up and running quickly.

Automated Machine Learning (AutoML) Industry Overview

The global automated machine learning market exhibits moderate fragmentation, with numerous players meeting market demands. Intensifying 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 industry leaders include Datarobot Inc., Amazon Web Services Inc., dotData Inc., IBM Corporation, and Dataiku.

In August 2023, DataRobot introduced a new generative artificial intelligence (AI) offering comprising platform capabilities and applied AI services designed to expedite the journey from concept to value with generative AI.

In August 2023, dotData Inc. launched dotData Ops, a next-generation no-code MLOps platform. This platform empowers ML engineers by delivering an intuitive, self-service environment for the efficient deployment and operationalization of data, feature, and prediction pipelines.

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 Assessment of the Impact of COVID-19 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 Users
    • 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