查看购物车
  • Traditional Chinese
  • English
  • Japanese
  • Korean
封面
市场调查报告书
商品编码
1692109

自动化机器学习：市场占有率分析、产业趋势与统计、成长预测（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个工作天内

价格

※ 本网页内容可能与最新版本有所差异。详细情况请与我们联繫。

简介目录

预计 2025 年自动机器学习市场价值为 25.9 亿美元，到 2030 年将达到 159.8 亿美元，预测期内（2025-2030 年）的复合年增长率为 43.9%。

自动机器学习-市场-IMG1

机器学习 (ML) 是人工智慧 (AI) 的一个分支，其中学习演算法使用统计方法进行分类和预测，揭示资料探勘计划中的关键见解。这些见解推动应用和业务决策，并理想地影响关键成长指标。这些解决方案围绕着演算法、模型和计算复杂性，因此需要由熟练的专家来开发。

主要亮点

  • 机器学习（ML）已成为一个必不可少的组成部分。然而，建立高效能机器学习应用程式需要高度专业化的资料科学家和领域专家。自动化机器学习 (AutoML) 旨在透过让领域专家自动建立机器学习应用程式来减少对资料科学家的需求，而无需大量的统计或机器学习知识。
  • 物联网、自动化和云端基础的服务的日益普及正在推动市场投资的增加。该解决方案使中小型企业和大型企业能够外包他们需要的一切，以提高资料品质、安全性、安全性和机器学习应对力，从而避免僱用资料科学资源的成本和挑战。该服务还得到了 Calligo 数据洞察平台的支持，该平台专为机器学习工作负载而建置。例如，2024 年 1 月，Google Cloud 和 Hugging Face 宣布建立策略伙伴关係，以加速生成式 AI 和 ML 开发。此次合作将使开发者能够将 Google Cloud 的基础架构用于所有 Hugging Face 服务，从而允许 Hugging Face 模式在 Google Cloud 上进行训练和使用。
  • 一些公司，例如 Facebook 和 Google，正在转向 AutoML 来自动化其内部流程，尤其是 ML 模型的建立。 Asimo 是 Facebook 的 AutoML 开发人员，可自动对目前模型进行改进。谷歌还发布了 AutoML 工具，该工具可以自动寻找最佳模型和设计机器学习演算法的过程。谷歌宣布了「Cloud AutoML」。 「Cloud AutoML」是一款产品，可协助缺乏机器学习（ML）专业知识的公司建立高品质、自订的人工智慧（AI）模型，为 Google 的产品和服务提供支援。 Cloud AutoML 使企业和开发人员能够根据他们的使用案例训练自订视觉模型。每家公司的这些创新都将推动市场的发展。
  • 受医疗保健领域应用和研究日益增多的推动，AutoML 市场预计将实现显着成长。随着 AutoML 彻底改变患者护理和医学研究，针对医疗保健挑战的 AI主导解决方案的需求正在激增。 AutoML 可自动执行模型选择和特征工程等复杂的机器学习任务，简化疾病诊断、治疗优化和药物发现的预测模型的开发。
  • 机器学习（ML）在许多应用中变得越来越普遍，但为了充分支援这种成长，我们需要更多的 ML 专家。自动化机器学习（AutoML）的目标是让机器学习更容易实现。因此，专家应该能够部署更多的机器学习系统，而且 AutoML 可能比直接使用 ML 所需的专业知识更少。然而，该技术的采用尚未深入，这限制了市场的成长。
  • 自从新冠肺炎疫情以来，随着企业利用智慧解决方案实现业务流程自动化，我们看到人工智慧的采用增加。预计这一趋势将在未来几年持续下去，进一步推动人工智慧在组织流程中的应用。

自动化机器学习市场趋势

BFSI 部门推动市场成长

  • 银行、金融服务和保险 (BFSI) 行业越来越多地采用 AI 和 ML 技术来提高业务效率并增强消费者体验。随着资料变得越来越重要，机器学习 BFSI 应用程式的需求量很大。自动化机器学习可以利用大量资料、经济的处理能力和经济的储存产生准确、快速的结果。
  • 此外，机器学习 (ML) 解决方案使金融公司能够透过智慧流程自动化来自动执行重复业务，透过聊天机器人提高企业生产力，实现管理流程自动化以及员工培训游戏化，从而取代手动任务。机器学习有望用于实现财务流程的自动化。
  • 疫情爆发后，金融机构更专注于透过数位管道接触并帮助客户。如今，金融领域出现了一系列数位解决方案，包括聊天机器人、开户和管理支援以及技术援助，Posh.Tech、Spixii 等公司现在提供智慧聊天机器人，旨在为银行提供面向客户的基本功能。
  • HDFC 银行正在使用由班加罗尔 Senseforth AI Research 开发的基于 AI 的聊天机器人「Eva」。自今年 3 月推出以来，Eva（电子虚拟助理的缩写）已经回覆了超过 270 万个客户咨询，与超过 53 万名独立用户进行了互动，并进行了 120 万次对话。德意志银行宣布与 NVIDIA 建立多年创新伙伴关係，以加速人工智慧 (AI) 和机器学习 (ML) 在金融领域的应用。
  • 银行面临越来越大的风险管理压力和更严格的管治和监管要求，它们必须改善服务产品以更好地服务客户。银行诈骗案件的增加预计将推动人工智慧和机器学习的采用。一些金融科技品牌越来越多地在多个管道的各种应用中使用人工智慧和机器学习，以利用可用的客户资料并预测客户需求如何变化，哪些诈骗活动最有可能袭击系统，哪些服务会有益，等等。
  • 23财年，印度储备银行（RBI）报告称，印度全国发生了超过13,000起银行诈骗案件，与上年度相比有所增加。这扭转了过去十年的趋势。银行诈骗的增加可能会刺激进一步的市场需求。

北美占据主要市场占有率

  • 北美预计将占据大部分市场份额，这得益于其强大的创新生态系统，该生态系统由联邦政府对先进技术的战略投资推动，并辅以来自全球各地有远见的科学家和企业家，以及推动自动机器学习（AutoML）发展的认可研究机构。
  • 政府，包括州和地方政府，处理大量以前以纸本形式储存并手动处理的公民资料。但随着人工智慧（AI）和机器学习技术提供更快、更准确的资料收集和处理方法，政府可以专注于更复杂、更长期的社会和文化问题。此外，协作式机器学习的商业应用日益增加预计将推动对 AutoML 的需求。
  • 据加拿大政府称，人工智慧（AI）技术有望增强加拿大政府向公民提供服务的方式。英国政府在审查人工智慧在政府计画和服务中的使用时，确保有明确的价值观、道德观和规则指南。
  • 当美国试图确立 AutoML 主导地位时，加拿大也在为此类发展做准备。例如，2023 年 4 月，ePayPolicy 宣布推出 Payables Connect，这是其保险支付和对帐产品套件的新功能。这将利用 ePay 现有的整合和机器学习技术，完全实现支付匹配、设计和支付的自动化。
  • 儘管加拿大仍处于各行业采用自动化机器学习的早期阶段，但预计有几个因素将推动市场成长，包括金融领域对自动化的需求日益增长以及学生对教育的兴趣日益浓厚。
  • 该地区的 AutoML 市场正被云端运算所改变。无伺服器运算使创作者能够快速推出和运行 ML 应用程式。例如，根据AWS的数据，2023年10月美国云端处理基础设施支出将超过1,080亿美元。
  • 此外，许多大大小小的组织都在从传统商业模式转型为数位商业模式。这种转变催生了混合云市场，因为它具有降低整体拥有成本（TCO）、提高安全性、灵活性和敏捷性等优势。 IBM 表示，89% 的 IT 领导者希望将业务关键型工作负载转移到云端，这一切都源自于数位化的提升。此类云端解决方案的扩展可能会进一步推动该地区的市场成长。

自动机器学习行业概览

全球自动机器学习市场呈现中度分散化，大量参与者满足市场需求。竞争是由新进入者的涌入推动的，促使现有进入者制定策略来扩大基本客群。这种动态情势也刺激了技术创新，现有市场参与者纷纷努力开发尖端产品。着名的市场领导包括 Datarobot Inc.、Amazon Web Services Inc.、dotData Inc.、IBM Corporation 和 Dataiku。

  • 2024 年 2 月领先的技术服务和顾问公司 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 和资料平台提供支持，其中包括 watsonx.data、watsonx.ai 和 watsonx.ai。管治和 AI 助理为客户提供可互通的服务，加速 AI 的采用。这项独特的服务为业务提供了涵盖工具、大型语言模型 (LLM)、简化流程和强大管治的功能。它也为基于 watsonx.data 和 AI 的未来企业分析解决方案奠定了基础。
  • 2024 年 5 月，Snapchat 推出了一系列尖端的扩增实境(AR) 和机器学习 (ML) 工具，旨在帮助品牌和广告商为使用者创造互动体验。该公司正在投资自动化和机器学习，以便品牌更快、更轻鬆地创建 AR 试穿资产。
  • 2023 年 9 月 富士通有限公司和 Linux 基金会在 2023 年 9 月于西班牙毕尔巴鄂举行的 2023 年欧洲开放原始码高峰会之前宣布将富士通的自动机器学习和 AI 公平性技术作为开放原始码软体(OSS)。这两个计划预计将为用户提供软体来自动生成自己的机器学习模型的程式码，以及解决训练资料中潜在偏差的技术。

其他福利

  • Excel 格式的市场预测 (ME) 表
  • 3 个月的分析师支持

目录

第 1 章 简介

  • 研究假设和市场定义
  • 研究范围

第二章调查方法

第三章执行摘要

第四章 市场动态

  • 市场驱动因素
    • 对高效诈欺侦测解决方案的需求日益增加
    • 对智慧业务流程的需求不断增加
  • 市场限制
    • 自动化机器学习工具的采用缓慢
  • 产业价值链分析
  • 产业吸引力-波特五力分析
    • 新进入者的威胁
    • 买家的议价能力
    • 供应商的议价能力
    • 替代品的威胁
    • 竞争对手之间的竞争强度
  • 主要宏观经济趋势将如何影响市场

第五章 市场区隔

  • 按解决方案
    • 独立或本地
    • 云
  • 按自动化类型
    • 资料处理
    • 特征工程
    • 造型
    • 视觉化
  • 按最终用户
    • BFSI
    • 零售与电子商务
    • 卫生保健
    • 製造业
    • 其他最终用户
  • 按地区
    • 北美洲
      • 美国
      • 加拿大
    • 欧洲
      • 英国
      • 德国
      • 法国
      • 其他欧洲国家
    • 亚太地区
      • 中国
      • 日本
      • 韩国
      • 其他亚太地区
    • 世界其他地区

第六章 竞争格局

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

第七章投资分析

第 8 章：市场的未来

简介目录
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