自动机器学习 (AutoML) 市场 - 2023 年至 2028 年预测

预计2021年自动化机器学习市场规模将达到653,805,000美元，复合年增长率为44.14%，到2028年将达到8,450,981,000美元。

自动化机器学习 (AutoML) 是使用人工智能 (AI) 算法自动构建、优化和部署机器学习模型的过程。这项技术允许公司以最少的人为干预自动构建预测模型。对AutoML 产品的需求不断增长，因为那些无法充分接触数据科学家或有限的机器学习专业知识的公司可以更好地了解其客户、产品和其他关键业务指标。这要归功于AutoML 在创建准确模型以快速做出预测方面的足智多谋和实用性。容易地。AutoML 通过同时设计优化模型性能所需的特征工程和超参数调整，自动为给定任务选择最佳 ML 算法。此外，您可以自动化模型部署和扩展以支持生产用例。AutoML 市场的增长是由于对速度、效率和准确性更高的机器学习解决方案的需求，以及当前数据科学专家的短缺以及整个行业越来越多地采用人工智能和云服务，从而推动了AutoML 市场的增长。并表示将同步推进。

对公司特定数据分析和预测的需求不断增加。

企业生成和收集的数据量的巨大增加增加了对数据分析和预测模型的需求。AutoML解决方案帮助企业快速、高效、准确地处理这些数据，使他们能够从数据中获得有价值的见解，从而为AutoML市场扩张创造机会。例如，PayPal 报告称，通过采用 H2O.ai 的 AutoML 工具，其欺诈检测模型的效率从 89% 提高到 94.7%。此外，采用DataRobot AutoML软件后，Lenovo的销售预测模型准确率提高了7.5%。此外，床上用品解决方案提供商 California Design Den 使用 Google 的 AutoML 工具将库存结转减少了约 50%。

AutoML 解决方案的高成本和有限的定制仍然是一个主要挑战。

AutoML 解决方案价格昂贵，这可能会限制采用，因为各种小型企业和企业需要权衡使用 AutoML 解决方案的收益和成本，以确保他们获得良好的投资回报。有性别之分。此外，AutoML解决方案在构建机器学习模型所涉及的自动化方面的定制非常有限，并且很难将非定制的AutoML解决方案与现有业务应用程序和工作流程集成，因此需要定制AutoML解决方案的企业的有效采用可能会受到限制。

从供应商来看，科技巨头占据了 AutoML 市场的最大份额。

Google、Amazon和Microsoft等科技巨头认识到对早期自动化机器学习解决方案不断增长的需求，并大力投资 AutoML 解决方案。例如，Microsoft Azure 提供基于云的 AutoML 解决方案，允许企业构建自定义机器学习模型，而无需广泛的技术专业知识，并在 ML 模型上执行特征工程、算法选择、超参数调整等各种功能。自动化一些施工中涉及的任务。此外，这些公司提供的软件平台效率的提高正在增加 AutoML 平台的消耗。

欺诈检测领域预计将占据自动化机器学习市场的大部分应用份额。

AutoML 被广泛用于通过数据准备、模型选择、超参数、集成技术等来构建 BFSI 和电子商务等各个行业中的欺诈检测预测模型。AutoML 执行数据清理和预处理任务，例如数据插补、缩放和特征工程，以确保用于欺诈检测的数据准确且一致。此外，通过调整 ML 模型的超参数并优化其在给定数据集上的性能，您可以确保欺诈检测模型稳健并且可以很好地推广到新数据。BFSI 运营的各种电子商务网站和公司的在线欺诈事件数量不断增加，对欺诈检测解决方案和模型产生了很高的需求，预计这将在预测期内增加该细分市场的市场份额。例如，2021 年 3 月，印度一家大型银行公司发生的欺诈事件金额达 4.92 万亿卢比。印度法医研究显示，2022 年 9 月增加了 3634.2 亿卢比。此外，领先的交易和支付服务公司 Worldline SA 估计，支付欺诈造成的损失相当于 2022 年电子商务供应商总销售额的 3.6%。

亚太地区占自动化机器学习市场的大部分，预计在预测期内将增长。

该地区零售和电子商务领域的快速发展预计将推动 AutoML 市场的增长。AutoML解决方案在零售行业被广泛采用，用于分析客户数据，构建需求预测、客户细分和个性化营销的预测模型，以改善零售商的客户体验并增加销售额。我们正在支持。

市场趋势：

• 2023 年 3 月，TDK 公司新收购的 Qeexo 推出了 Arm Keil，这是一个基于 Arm(R) 架构的微控制器编程平台，采用设计、构建和测试嵌入式应用程序所需的各种工具。 MDK的新集成自动化ML 平台。
• 2022年10月，通过其云平台提供合规和安全服务的Qualys Inc.宣布收购云安全公司Blue Hexagon的AutoML和AI软件，为使用Qualys云平台的消费者提供数据集成和AI软件，提供数据洞察能力。
• 2021 年 9 月，生产自动化机器学习技术的公司 Big Squid 被 Qlik Technologies 收购。该公司提供集成和数据分析服务，通过集成 AutoML 和 AI 技术来增强 Qlik Technologies 提供的预测分析解决方案。

目录

第 1 章 简介

• 市场概况
• 市场定义
• 调查范围
• 市场细分
• 货币
• 先决条件
• 基准年和预测年的时间表

第二章研究方法论

• 调查数据
• 调查过程

第三章执行摘要

• 调查亮点

第四章市场动态

• 市场驱动力
• 市场製约因素
• 波特五力分析
• 行业价值链分析

5. 按提供商划分的自动化机器学习 (AutoML) 市场

• 介绍
• 开源
• 启动
• 科技巨头

第 6 章 自动机器学习 (AutoML) 市场：按应用分类

• 介绍
• 欺诈识别
• AML检测
• 价钱
• 营销和销售管理
• 其他

第 7 章 自动机器学习 (AutoML) 市场：按地区

• 介绍
• 美洲
  • 美国
  • 其他
• 欧洲、中东/非洲
  • 德国
  • 法国
  • 英国
  • 其他
• 亚太地区
  • 中国
  • 日本
  • 韩国
  • 其他

第八章竞争格局与分析

• 主要公司及战略分析
• 初创企业和市场盈利能力
• 合併、收购、协议与合作
• 供应商竞争力矩阵

第九章公司简介

• IBM
• Microsoft Corporation
• Amazon Web Services
• Oracle
• Alphabet Inc.（Google）
• Databricks
• Qlik
• Akkio Inc.
• Obviously AI, Inc.

The automated machine learning market size was valued at US$653.805 million in 2021 and is expected to grow at a CAGR of 44.14% to reach US$8,450.981 million by 2028.

Automated Machine Learning (AutoML) is a process of using Artificial Intelligence (AI) algorithms to automate the process of building, optimizing, and deploying machine learning models. It is a technology that enables businesses to build predictive models with minimal human intervention automatically. The rising demand for autoML products can be attributed to the resourcefulness and usefulness of autoML in creating accurate models to make better predictions about customers, products, or other important business metrics quickly and easily for businesses that do not have proper access to data scientists or have limited expertise in machine learning to. AutoML works by automating the selection of the best ML algorithms for a given task by simultaneously designing the feature engineering and hyperparameter tuning required to optimize model performance. In addition, it can automate the deployment and scaling of models to support production use cases. The growth of the AutoML market is expected to be driven by the need for machine learning solutions with enhanced speed, efficiency, and accuracy, combined with the existing shortage of data science experts and the increasing adoption of AI and cloud services across industries.

Increasing need for data analysis and prediction by companies.

The massive increase in the amount of data generated and collected by companies is growing the demand for data analysis and prediction models, which is creating an opportunity for the expansion of the autoML market as AutoML solutions help companies to process this data quickly, efficiently and accurately, enabling them to extract valuable insights from their data. For instance, PayPal company reported that the efficiency of its fraud detection model increased from 89% to 94.7% through the adoption of H2O.ai's AutoML tool. In addition, the sales prediction model of Lenovo company witnessed an increase in accuracy by 7.5% after the adoption of autoML software by DataRobot Company. Further, California Design Den, a company providing bedding solutions, lowered its inventory carryover by approximately 50% by using the autoML tool offered by Google.

The high cost and limited customization of autoML solutions remain a significant challenge.

AutoML solutions are expensive, which could restrain their adoption by various small and medium-sized firms and businesses as they need to weigh the benefits of using AutoML solutions against the cost to ensure that the return on investment is sufficient. Further, AutoML solutions are highly limited in customization in automating involved in building machine learning models, which limits their adoption by businesses that require effectively customized AutoML solutions since integrating non-customized AutoML solutions with existing business applications and workflows can be challenging.

By provider, the tech-giants sector holds the most significant portion of the autoML market.

Tech giants like Google, Amazon, and Microsoft have invested heavily in AutoML solutions by recognizing the growing demand for automated ML solutions at the initial stage. For instance, Microsoft Azure offers a cloud-based AutoML solution that enables businesses to build custom machine learning models without requiring extensive technical expertise to automate several tasks involved in building ML models, including feature engineering, algorithm selection, and hyperparameter tuning. Further, the increase in the efficiency of the software platforms offered by these companies is increasing the consumption of their autoML platforms.

The fraud detection segment is expected to have a major share of the automated machine learning market by application.

AutoML is extensively adopted to build predictive models for fraud detection in different industries, such as the BFSI and e-commerce, through data preparation, model selection, hyperparameter, and ensemble methods. AutoML performs data cleaning and preprocessing tasks such as data imputation, scaling, and feature engineering, ensuring that the data used for fraud detection is accurate and consistent. In addition, it can tune the hyperparameters of ML models to optimize their performance on a given dataset to ensure that the fraud detection models are robust and can generalize appropriately to new data. The rising incidents of online fraud in various e-commerce sites and companies operating in the BFSI are expected to increase the market share of this sector over the forecast period as it is generating a high demand for fraud detection solutions and models. For instance, fraud incidents among major banking companies in India in March 2021 amounted to Rs.4.92 trillion. It increased by Rs. 36342 crores during September 2022, as per research conducted by Indiaforensic. In addition, Worldline SA, a leading company offering transaction and payment services, estimated that payment fraud created a loss of 3.6% of the total sales made by e-commerce vendors in 2022.

Asia Pacific region holds a significant portion of the auto machine learning market and is expected to grow in the forecast period.

The rapid advancement of the retail and e-commerce sector in the region is expected to promote the growth of the autoML market as AutoML solutions are being extensively adopted in the retail industry to build predictive models for demand forecasting, customer segmentation, and personalized marketing by analyzing customer data to help retailers improve customer experiences and increase sales.

Market Developments:

• In March 2023, a newly acquired company by TDK Corporation, Qeexo, released a new integrated auto ML platform for Arm Keil MDK, a programming platform for microcontrollers based on the Arm® architecture adopted with different tools required to design, construct, and test embedded applications.
• In October 2022, Qualys Inc., a company providing compliance and security services through its cloud platform, declared the acquisition of autoML and AI software of Blue Hexagon, a cloud security company, to provide data integration and data insight features to consumers using Qualys Cloud Platform.
• In September 2021, Big Squid, a company producing automated ML technology, was acquired by Qlik Technologies. This company offers integration and data analytics services to enhance the predictive analysis solution offered by Qlik Technologies by integrating autoML and AI technology.

Market Segmentation:

By Provider

• Open Source
• Startups
• Tech Giants

By Application

• Fraud Detection
• AML Detection
• Pricing
• Marketing and Sales Management
• Others

By Geography

• Americas
• USA
• Others
• Europe Middle East and Africa
• Germany
• France
• United Kingdom
• Others
• Asia Pacific
• China
• Japan
• South Korea
• Others

TABLE OF CONTENTS

1. INTRODUCTION

• 1.1. Market Overview
• 1.2. Market Definition
• 1.3. Scope of the Study
• 1.4. Market Segmentation
• 1.5. Currency
• 1.6. Assumptions
• 1.7. Base, and Forecast Years Timeline

2. RESEARCH METHODOLOGY

• 2.1. Research Data
• 2.2. Research Process

3. EXECUTIVE SUMMARY

• 3.1. Research Highlights

4. MARKET DYNAMICS

• 4.1. Market Drivers
• 4.2. Market Restraints
• 4.3. Porter's Five Force Analysis
  • 4.3.1. Bargaining Power of Suppliers
  • 4.3.2. Bargaining Power of Buyers
  • 4.3.3. Threat of New Entrants
  • 4.3.4. Threat of Substitutes
  • 4.3.5. Competitive Rivalry in the Industry
• 4.4. Industry Value Chain Analysis

5. AUTOMATED MACHINE LEARNING (AUTOML) MARKET, BY PROVIDER

• 5.1. Introduction
• 5.2. Open Source
• 5.3. Startups
• 5.4. Tech Giants

6. AUTOMATED MACHINE LEARNING (AUTOML) MARKET, BY APPLICATION

• 6.1. Introduction
• 6.2. Fraud Detection
• 6.3. AML Detection
• 6.4. Pricing
• 6.5. Marketing and Sales Management
• 6.6. Others

7. AUTOMATED MACHINE LEARNING (AUTOML) MARKET, BY GEOGRAPHY

• 7.1. Introduction
• 7.2. Americas
  • 7.2.1. USA
  • 7.2.2. Others
• 7.3. Europe, Middle East and Africa
  • 7.3.1. Germany
  • 7.3.2. France
  • 7.3.3. United Kingdom
  • 7.3.4. Others
• 7.4. Asia Pacific
  • 7.4.1. China
  • 7.4.2. Japan
  • 7.4.3. South Korea
  • 7.4.4. Others

8. COMPETITIVE ENVIRONMENT AND ANALYSIS

• 8.1. Major Players and Strategy Analysis
• 8.2. Emerging Players and Market Lucrativeness
• 8.3. Mergers, Acquisitions, Agreements, and Collaborations
• 8.4. Vendor Competitiveness Matrix

9. COMPANY PROFILES

• 9.1. IBM
• 9.2. Microsoft Corporation
• 9.3. Amazon Web Services
• 9.4. Oracle
• 9.5. Alphabet Inc. (Google)
• 9.6. Databricks
• 9.7. Qlik
• 9.8. Akkio Inc.
• 9.9. Obviously AI, Inc.