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市场调查报告书
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1284265

到 2028 年的自动机器学习 (AutoML) 市场预测 - 按产品（平台和服务）、按部署类型（云和本地）、按自动化类型、按公司规模、按应用程序、按最终用户、按地区世界分析

Automated Machine Learning (AutoML) Market Forecasts to 2028 - Global Analysis By Offering (Platform and Service), Deployment Type (Cloud and On-Premises), Automation Type, Enterprise Size, Application, End User and By Geography
出版日期: | 出版商: Stratistics Market Research Consulting | 英文 175+ Pages | 商品交期: 2-3个工作天内

价格

根据 Stratistics MRC，2022 年全球自动化机器学习 (AutoML) 市场将达到 8.2 亿美元，预测期内復合年增长率为 44.8%，预计达到 7.58 美元十亿。

自动机器学习 (AutoML) 是将机器学习生命週期中较为复杂和基本的步骤自动化的过程。 这使得在没有任何理论背景或先前机器学习专业知识的情况下也很容易参与人工智能开发。 这对初学者和高级 AI 从业者都有好处。 用户可以将数据上传到训练算法，让系统自动为特定问题选择合适的神经网络设计。 AutoML 促进了效率、可扩展性和重复错误的消除。

根据 O'Reilly 的一项调查，只有 20% 的受访者表示他们使用自动化机器学习工具，48% 的受访者表示他们从未听说过该技术。

市场动态：

驱动程序

用户友好型机器学习软件的发展

专家级机器学习知识供不应求。 这也体现在符合条件的申请人数明显高于职位空缺数。 AutoML 试图通过自动化非专家无法完成的程序来填补这一空白。 AutoML 是一种用户友好的机器学习程序，具有易于理解的界面，因此具有基本技术知识的任何人和学习者都可以使用它。

约束

缺乏熟练的专业知识

AutoML 的平台要求用户具有扎实的编程、数据科学和机器学习背景。 找到创建、实施和管理 AutoML 模型所需的技能对公司来说是一个挑战。 任何使用 AutoML 平台的人都应该不断提高自己的技能，并及时了解行业的最新发展。 由于人才短缺，企业面临来自竞争对手的激烈竞争。 数据管理、数据可视化和云计算方面的技能短缺阻碍了市场扩张。

机会

提高运营效率并降低成本

机器学习的普及正在帮助公司降低成本。 通过采用 AutoML 解决方案，公司可以降低投资于昂贵的基础设施和聘请专家的成本。 此外，人工智能解决方案的快速开发和部署提高了运营效率并加强了决策。 机器学习的大众化使公司能够扩展其服务范围并开拓新市场，从而增加利润和市场份额。

威胁

对 AutoML 的认知度较低

儘管 AutoML 具有提高准确性、可扩展性和效率等诸多优势，但许多公司仍对引入它犹豫不决。 许多高管和决策者可能不知道 AutoML 的好处及其对您的业务的影响。 自动化机器学习 (AutoML) 解决方案缺乏渗透是市场扩张的主要障碍。

COVID-19 的影响：

在 COVID-19 爆发期间，组织越来越依赖智能解决方案来实现企业流程自动化。 用于识别 COVID-19 实例的技术。 它不仅在病毒药物的开发方面表现出色，而且在诊断、预后和流行病预测方面也表现出色。 我们开发了许多机器学习 (ML) 模型来估计 COVID-19 存活的可能性，使用自动机器学习 (autoML) 对它们进行比较，并确定最佳模型。 它已经发展成为一种工具，可以帮助医生在医院对患者进行分层。

预计在预测期内数据处理部门将是最大的

据估计，数据处理行业将实现有利可图的增长。 查找和修復数据问题的过程可以通过 autoML 实现自动化。 这包括查找缺失的数字、修復数据格式问题和删除异常值。 它还包括可以自动应用于您的数据的技术，例如规范化和标准化。 通过将数据转换成更好的格式，错误和差异就不太可能发生。 与手动处理数据相比，它花费的时间和精力更少。

云部分预计在预测期内表现出最高的复合年增长率。

由于互联网连接的增加，云部分预计在预测期内以最快的复合年增长率增长。 基于云的 AutoML 系统提供了更大的可扩展性和灵活性。 随着工作负载和数据量的变化，您可以根据需要轻鬆扩大或缩小规模。 此外，计费系统往往采用按量付费的方式，对于工作量波动较大的业务来说更为经济。 它不需要初始投资，并以合理的价格提供完整的功能。

市场份额最大的地区

预计在预测期内，北美将占据最大的市场份额。 这极大地促进了自动化机器学习市场的增长和发展。 美国是该地区最发达的国家之一。 AutoML 行业在美国迅速扩张，多家领先公司提供从全自动平台到帮助数据科学家创建机器学习模型的工具等各种产品和服务。 在美国，AutoML 解决方案的使用显着增加，尤其是在医疗保健、银行和零售等行业。

复合年增长率最高的地区：

由于技术不断进步，预计亚太地区在预测期内的复合年增长率最高。 亚太国家是 IT 外包的首选目的地。 经济的快速增长、对 IT 基础设施的投资增加、创新技术的采用增加以及政府为推进 AI 技术所做的更多努力被认为是推动该地区市场增长的因素。。

主要发展：

2023 年 2 月，IBM 将把 StepZen 的技术整合到其产品组合中，为客户提供构建、连接和管理 API 和数据源的端到端解决方案，以加快创新并从数据中创造更大的价值。

2022 年 11 月，Amazon Web Services, Inc. 开设了第二个设施，即 AWS 亚太地区（海得拉巴）区域，以加强其对印度客户的服务。 它将成为印度 AWS 供应链的一部分，包括该国更广泛经济中的建筑、设备维护、工程、电信和就业。

2022 年 11 月，微软将通过 Amazon.in、Reliance Digital、Croma、Vijay Sales 和部分多品牌商店在印度推出其新的 Surface 产品 Surface Laptop 5 和 Surface Pro 9 的预购。宣布开始 随着新 Surface 产品的发布，Microsoft 的精华汇集在一台设备中，每个人都可以参与、被看到、被听到并表达他们的创造力。

2022 年 10 月，Oracle 将与 NVIDIA 合作，为客户提供 Nvidia GPU 用于机器学习工作负载的访问权限，从而增强 Oracle 机器学习工具的性能和功能。

2022 年 6 月，Google LLC 将更名为 Google Public Sector，这是 Google 的一个新部门，专注于帮助美国公共机构（包括联邦、州、地方政府和教育机构）加速数字化转型。随着成立，我们宣布我们将扩大在美国的努力。

本报告内容

  • 按地区和国家/地区划分的市场份额评估
  • 向新进入者提出战略建议
  • 2020、2021、2022、2025 和 2028 年的综合市场数据
  • 市场趋势（驱动因素、制约因素、机会、威胁、挑战、投资机会、建议）
  • 根据市场预测在关键业务领域提出战略建议
  • 竞争格局映射关键共同趋势。
  • 公司简介，包括详细的战略、财务状况和近期发展
  • 映射最新技术进步的供应链趋势

免费定制优惠：

购买此报告的客户将获得以下免费定制选项之一：

  • 公司简介
    • 其他市场参与者的综合概况（最多 3 家公司）
    • 主要参与者的 SWOT 分析（最多 3 家公司）
  • 区域细分
    • 根据客户的要求对主要国家/地区的市场估计/预测/复合年增长率（注意：基于可行性检查）。
  • 竞争基准
    • 根据产品组合、区域影响力和战略联盟对主要参与者进行基准测试

内容

第 1 章执行摘要

第二章前言

  • 概览
  • 利益相关者
  • 调查范围
  • 调查方法
    • 数据挖掘
    • 数据分析
    • 数据验证
    • 研究方法
  • 调查来源
    • 主要研究来源
    • 二级研究来源
    • 假设

第三章市场趋势分析

  • 司机
  • 约束因素
  • 机会
  • 威胁
  • 应用分析
  • 最终用户分析
  • 新兴市场
  • COVID-19 的影响

第 4 章波特五力分析

  • 供应商的议价能力
  • 买家的议价能力
  • 替代品的威胁
  • 新进入者的威胁
  • 竞争公司之间的敌对关係

第 5 章全球自动机器学习 (AutoML) 市场：按产品分类

  • 平台
  • 服务

第 6 章全球自动机器学习 (AutoML) 市场：按部署类型

  • 云
  • 本地

第 7 章全球自动化机器学习 (AutoML) 市场：按自动化类型

  • 特征工程
  • 信息处理
  • 优化和调整超参数
  • 模型合奏
  • 模型选择
  • 可视化

第 8 章。全球自动机器学习 (AutoML) 市场：按公司规模分类

  • 小型企业
  • 中型公司
  • 大公司

第 9 章。全球自动机器学习 (AutoML) 市场：按应用

  • 欺诈检测
  • 体检
  • 销售和营销管理
  • 传输优化
  • 其他用途

第 10 章全球自动机器学习 (AutoML) 市场：按最终用户分类

  • BFSI
  • 医疗保健
  • IT 与电信
  • 零售
  • 电子商务
  • 製造业
  • 政府
  • 其他最终用户

第 11 章全球自动机器学习 (AutoML) 市场：按地区

  • 北美
    • 美国
    • 加拿大
    • 墨西哥
  • 欧洲
    • 德国
    • 英国
    • 意大利
    • 法国
    • 西班牙
    • 其他欧洲
  • 亚太地区
    • 日本
    • 中国
    • 印度
    • 澳大利亚
    • 新西兰
    • 韩国
    • 其他亚太地区
  • 南美洲
    • 阿根廷
    • 巴西
    • 智利
    • 其他南美洲
  • 中东和非洲
    • 沙特阿拉伯
    • 阿拉伯联合酋长国
    • 卡塔尔
    • 南非
    • 其他中东和非洲地区

第12章主要发展

  • 合同、伙伴关係、协作和合资企业
  • 收购与合併
  • 新产品发布
  • 业务扩展
  • 其他关键策略

第13章公司简介

  • Amazon Web Services Inc
  • DataRobot Inc.
  • Qlik Technologies Inc
  • Microsoft Corporation
  • dotData Inc
  • Gnosis DA S.A.
  • SAS Institute Inc
  • Google LLC
  • H2O.ai Inc
  • TAZI AI
  • RapidMiner
  • Squark
  • BigML Inc
  • Determined.ai Inc
  • Dataiku
  • IBM Corporation
  • EdgeVerve Systems Limited
  • Oracle
  • Enhencer LLC
Product Code: SMRC23097

According to Stratistics MRC, the Global Automated Machine Learning (AutoML) Market is accounted for $0.82 billion in 2022 and is expected to reach $7.58 billion by 2028 growing at a CAGR of 44.8% during the forecast period. Automated machine learning (AutoML) is a process that automates the more complex or basic steps of the machine-learning lifecycle. This makes it easier for people to engage in the development of AI without having a theoretical background or any prior expertise with machine learning. It benefits both the beginners and advanced AI practitioners. Users may upload data to training algorithms and have the system automatically choose the appropriate neural network design for a particular problem. Efficiency, scalability, and the elimination of recurring mistakes are all facilitated via AutoML.

According to a survey by O'Reilly found that only 20% of respondents reported using automated machine learning tools, while 48% had never heard of the technology.

Market Dynamics:

Driver:

Growing user-friendly machine learning software

Expert-level machine learning knowledge is in high demand, yet there is a shortage. This may be seen in the fact that there are considerably more competent applicants than there are vacant positions. AutoML intends to close this gap by automating procedures that would otherwise be beyond the capabilities of anybody except a subject-matter expert. Anyone with basic technical expertise and learners may use AutoML as it is a user-friendly machine learning program with straightforward interfaces.

Restraint:

Shortage of skilled expertise

AutoML platforms demand users with solid backgrounds in programming, data science, and machine learning. Finding the necessary skills to create, implement, and manage AutoML models is a challenge for businesses. People that use AutoML platforms must always improve their skills and stay aware of the most recent developments in the industry. Due to a lack of qualified candidates, businesses are in severe rivalry with their competitors. The expansion of the market is being hampered by the skill scarcity in the fields of data management, data visualization, and cloud computing.

Opportunity:

Raising operational efficiency & cost savings

Machine learning is becoming more widely available, which resulted in huge cost reductions for enterprises. Businesses may save the expenses of investing in expensive infrastructure and employing specialist people by adopting AutoML solutions. Additionally, quicker AI solution development and implementation is boosting operational effectiveness and enhancing decision-making. Businesses may extend their offers and tap into new markets owing to the democratization of machine learning, which boosts profits and market share.

Threat:

Lack of awareness on AutoML

Many businesses are hesitant to implement AutoML despite its numerous benefits, including improved accuracy, scalability, and efficiency. The advantages of AutoML and the potential effects it might have on businesses may not be well-known to many corporate executives and decision-makers. The poor uptake of automated machine learning (AutoML) solutions is a major barrier to the market's expansion.

COVID-19 Impact:

Organizations have relied more on intelligent solutions to automate their corporate processes during the COVID-19 outbreak. It is used in the methods for identifying COVID-19 instances. It has excelled in the areas of viral drug development as well as diagnostics, prognosis assessment, and epidemic forecasting. Numerous machine learning (ML) models that estimate the likelihood that a patient will survive a COVID-19 infection have been developed and compared using automated machine learning (autoML), and the top model has been determined. It evolved into a helpful tool for physicians to stratify patients in hospitals.

The data processing segment is expected to be the largest during the forecast period

The data processing segment is estimated to have a lucrative growth. The process of finding and fixing data problems may be automated with autoML. This involves finding missing numbers, fixing formatting issues with the data, and eliminating outliers. It involves methods that can be automatically applied to the data, such as normalization and standardization. By transforming the data into a more suitable format, the likelihood of mistakes and inconsistencies is decreased. It takes less time and effort to process data manually.

The cloud segment is expected to have the highest CAGR during the forecast period

The cloud segment is anticipated to witness the fastest CAGR growth during the forecast period, due to increasing internet connections. AutoML systems that are cloud-based provide more scalability and flexibility. When the workload or amount of data varies, they can simply scaled up or down as necessary. They often provide a pay-as-you-go pricing structure, which can be more economical for businesses with fluctuating workloads. It offers complete capability at a fair price with no initial outlay of funds.

Region with largest share:

North America is projected to hold the largest market share during the forecast period. It has significantly aided in the growth and development of the market for automated machine learning. US is one of the most developed countries in the region. In the US, the AutoML industry is expanding quickly, with several major businesses providing a range of products and services, from completely automated platforms to tools that help data scientists create machine learning models. In the US, usage of AutoML solutions has significantly increased, particularly in sectors like healthcare, banking, and retail.

Region with highest CAGR:

Asia Pacific is projected to have the highest CAGR over the forecast period, owing to its growing technological advancements. APAC countries are the most preferred destination for IT outsourcing. The rapid economic expansion, rising investments in IT infrastructure, growing uptake of innovative technologies, and expanding number of government efforts for the advancement of AI technologies may all be attributed to the market growth in this area.

Key players in the market:

Some of the key players profiled in the Automated Machine Learning (AutoML) Market include Amazon Web Services Inc, DataRobot Inc., Qlik Technologies Inc, Microsoft Corporation, dotData Inc, Gnosis DA S.A., SAS Institute Inc, Google LLC, H2O.ai Inc, TAZI AI, RapidMiner, Squark, BigML Inc, Determined.ai Inc, Dataiku, IBM Corporation, EdgeVerve Systems Limited, Oracle and Enhencer LLC.

Key Developments:

In February 2023, IBM integrated StepZen's technology into its portfolio, with the aims to provide its clients with an end-to-end solution for building, connecting, and managing APIs and data sources, enabling them to innovate faster and generate more value from their data.

In November 2022, Amazon Web Services, Inc. has launched AWS Asia Pacific (Hyderabad) Region, its second such facility to augment services to customers in India. The jobs will be part of the AWS supply chain in India, including construction, facility maintenance, engineering, telecommunications and jobs within the country's broader economy.

In November 2022, Microsoft announced that pre-orders for new Surface products, Surface Laptop 5 and Surface Pro 9, will commence in India via Amazon.in, Reliance Digital, Croma, Vijay Sales and select multi brand stores. The new Surface product launches bring the best of Microsoft together on a single device, enabling all users to participate, be seen, heard, and express their creativity.

In October 2022, Oracle partnered with NVIDIA, which enabled Oracle to offer its customers access to Nvidia's GPUs for use in machine learning workloads, enhancing the performance and capabilities of Oracle's machine learning tools.

In June 2022, Google LLC announced the expansion of its commitment in the United States with the creation of Google Public Sector, a new Google division that will focus on helping U.S. public sector institutions-including federal, state, and local governments, and educational institutions-accelerate their digital transformations.

Offerings Covered:

  • Platform
  • Service

Deployment Types Covered:

  • Cloud
  • On-Premises

Automation Types Covered:

  • Feature Engineering
  • Data Processing
  • Hyperparameter Optimization & Tuning
  • Model Ensembling
  • Model Selection
  • Visualization

Enterprise Sizes Covered:

  • Small Enterprise
  • Medium Enterprise
  • Large Enterprise

Applications Covered:

  • Fraud Detection
  • Medical Testing
  • Sales & Marketing Management
  • Transport Optimization
  • Other Applications

End Users Covered:

  • BFSI
  • Healthcare
  • IT & Telecom
  • Retail
  • E-Commerce
  • Manufacturing
  • Government
  • Other End Users

Regions Covered:

  • North America
    • US
    • Canada
    • Mexico
  • Europe
    • Germany
    • UK
    • Italy
    • France
    • Spain
    • Rest of Europe
  • Asia Pacific
    • Japan
    • China
    • India
    • Australia
    • New Zealand
    • South Korea
    • Rest of Asia Pacific
  • South America
    • Argentina
    • Brazil
    • Chile
    • Rest of South America
  • Middle East & Africa
    • Saudi Arabia
    • UAE
    • Qatar
    • South Africa
    • Rest of Middle East & Africa

What our report offers:

  • Market share assessments for the regional and country-level segments
  • Strategic recommendations for the new entrants
  • Covers Market data for the years 2020, 2021, 2022, 2025, and 2028
  • Market Trends (Drivers, Constraints, Opportunities, Threats, Challenges, Investment Opportunities, and recommendations)
  • Strategic recommendations in key business segments based on the market estimations
  • Competitive landscaping mapping the key common trends
  • Company profiling with detailed strategies, financials, and recent developments
  • Supply chain trends mapping the latest technological advancements

Free Customization Offerings:

All the customers of this report will be entitled to receive one of the following free customization options:

  • Company Profiling
    • Comprehensive profiling of additional market players (up to 3)
    • SWOT Analysis of key players (up to 3)
  • Regional Segmentation
    • Market estimations, Forecasts and CAGR of any prominent country as per the client's interest (Note: Depends on feasibility check)
  • Competitive Benchmarking
    • Benchmarking of key players based on product portfolio, geographical presence, and strategic alliances

Table of Contents

1 Executive Summary

2 Preface

  • 2.1 Abstract
  • 2.2 Stake Holders
  • 2.3 Research Scope
  • 2.4 Research Methodology
    • 2.4.1 Data Mining
    • 2.4.2 Data Analysis
    • 2.4.3 Data Validation
    • 2.4.4 Research Approach
  • 2.5 Research Sources
    • 2.5.1 Primary Research Sources
    • 2.5.2 Secondary Research Sources
    • 2.5.3 Assumptions

3 Market Trend Analysis

  • 3.1 Introduction
  • 3.2 Drivers
  • 3.3 Restraints
  • 3.4 Opportunities
  • 3.5 Threats
  • 3.6 Application Analysis
  • 3.7 End User Analysis
  • 3.8 Emerging Markets
  • 3.9 Impact of Covid-19

4 Porters Five Force Analysis

  • 4.1 Bargaining power of suppliers
  • 4.2 Bargaining power of buyers
  • 4.3 Threat of substitutes
  • 4.4 Threat of new entrants
  • 4.5 Competitive rivalry

5 Global Automated Machine Learning (AutoML) Market, By Offering

  • 5.1 Introduction
  • 5.2 Platform
  • 5.3 Service

6 Global Automated Machine Learning (AutoML) Market, By Deployment Type

  • 6.1 Introduction
  • 6.2 Cloud
  • 6.3 On-Premises

7 Global Automated Machine Learning (AutoML) Market, By Automation Type

  • 7.1 Introduction
  • 7.2 Feature Engineering
  • 7.3 Data Processing
  • 7.4 Hyperparameter Optimization & Tuning
  • 7.5 Model Ensembling
  • 7.6 Model Selection
  • 7.7 Visualization

8 Global Automated Machine Learning (AutoML) Market, By Enterprise Size

  • 8.1 Introduction
  • 8.2 Small Enterprise
  • 8.3 Medium Enterprise
  • 8.4 Large Enterprise

9 Global Automated Machine Learning (AutoML) Market, By Application

  • 9.1 Introduction
  • 9.2 Fraud Detection
  • 9.3 Medical Testing
  • 9.4 Sales & Marketing Management
  • 9.5 Transport Optimization
  • 9.6 Other Applications

10 Global Automated Machine Learning (AutoML) Market, By End User

  • 10.1 Introduction
  • 10.2 BFSI
  • 10.3 Healthcare
  • 10.4 IT & Telecom
  • 10.5 Retail
  • 10.6 E-Commerce
  • 10.7 Manufacturing
  • 10.8 Government
  • 10.9 Other End Users

11 Global Automated Machine Learning (AutoML) Market, By Geography

  • 11.1 Introduction
  • 11.2 North America
    • 11.2.1 US
    • 11.2.2 Canada
    • 11.2.3 Mexico
  • 11.3 Europe
    • 11.3.1 Germany
    • 11.3.2 UK
    • 11.3.3 Italy
    • 11.3.4 France
    • 11.3.5 Spain
    • 11.3.6 Rest of Europe
  • 11.4 Asia Pacific
    • 11.4.1 Japan
    • 11.4.2 China
    • 11.4.3 India
    • 11.4.4 Australia
    • 11.4.5 New Zealand
    • 11.4.6 South Korea
    • 11.4.7 Rest of Asia Pacific
  • 11.5 South America
    • 11.5.1 Argentina
    • 11.5.2 Brazil
    • 11.5.3 Chile
    • 11.5.4 Rest of South America
  • 11.6 Middle East & Africa
    • 11.6.1 Saudi Arabia
    • 11.6.2 UAE
    • 11.6.3 Qatar
    • 11.6.4 South Africa
    • 11.6.5 Rest of Middle East & Africa

12 Key Developments

  • 12.1 Agreements, Partnerships, Collaborations and Joint Ventures
  • 12.2 Acquisitions & Mergers
  • 12.3 New Product Launch
  • 12.4 Expansions
  • 12.5 Other Key Strategies

13 Company Profiling

  • 13.1 Amazon Web Services Inc
  • 13.2 DataRobot Inc.
  • 13.3 Qlik Technologies Inc
  • 13.4 Microsoft Corporation
  • 13.5 dotData Inc
  • 13.6 Gnosis DA S.A.
  • 13.7 SAS Institute Inc
  • 13.8 Google LLC
  • 13.9 H2O.ai Inc
  • 13.10 TAZI AI
  • 13.11 RapidMiner
  • 13.12 Squark
  • 13.13 BigML Inc
  • 13.14 Determined.ai Inc
  • 13.15 Dataiku
  • 13.16 IBM Corporation
  • 13.17 EdgeVerve Systems Limited
  • 13.18 Oracle
  • 13.19 Enhencer LLC

List of Tables

  • Table 1 Global Automated Machine Learning (AutoML) Market Outlook, By Region (2020-2028) ($MN)
  • Table 2 Global Automated Machine Learning (AutoML) Market Outlook, By Offering (2020-2028) ($MN)
  • Table 3 Global Automated Machine Learning (AutoML) Market Outlook, By Platform (2020-2028) ($MN)
  • Table 4 Global Automated Machine Learning (AutoML) Market Outlook, By Service (2020-2028) ($MN)
  • Table 5 Global Automated Machine Learning (AutoML) Market Outlook, By Deployment Type (2020-2028) ($MN)
  • Table 6 Global Automated Machine Learning (AutoML) Market Outlook, By Cloud (2020-2028) ($MN)
  • Table 7 Global Automated Machine Learning (AutoML) Market Outlook, By On-Premises (2020-2028) ($MN)
  • Table 8 Global Automated Machine Learning (AutoML) Market Outlook, By Automation Type (2020-2028) ($MN)
  • Table 9 Global Automated Machine Learning (AutoML) Market Outlook, By Feature Engineering (2020-2028) ($MN)
  • Table 10 Global Automated Machine Learning (AutoML) Market Outlook, By Data Processing (2020-2028) ($MN)
  • Table 11 Global Automated Machine Learning (AutoML) Market Outlook, By Hyperparameter Optimization & Tuning (2020-2028) ($MN)
  • Table 12 Global Automated Machine Learning (AutoML) Market Outlook, By Model Ensembling (2020-2028) ($MN)
  • Table 13 Global Automated Machine Learning (AutoML) Market Outlook, By Model Selection (2020-2028) ($MN)
  • Table 14 Global Automated Machine Learning (AutoML) Market Outlook, By Visualization (2020-2028) ($MN)
  • Table 15 Global Automated Machine Learning (AutoML) Market Outlook, By Enterprise Size (2020-2028) ($MN)
  • Table 16 Global Automated Machine Learning (AutoML) Market Outlook, By Small Enterprise (2020-2028) ($MN)
  • Table 17 Global Automated Machine Learning (AutoML) Market Outlook, By Medium Enterprise (2020-2028) ($MN)
  • Table 18 Global Automated Machine Learning (AutoML) Market Outlook, By Large Enterprise (2020-2028) ($MN)
  • Table 19 Global Automated Machine Learning (AutoML) Market Outlook, By Application (2020-2028) ($MN)
  • Table 20 Global Automated Machine Learning (AutoML) Market Outlook, By Fraud Detection (2020-2028) ($MN)
  • Table 21 Global Automated Machine Learning (AutoML) Market Outlook, By Medical Testing (2020-2028) ($MN)
  • Table 22 Global Automated Machine Learning (AutoML) Market Outlook, By Sales & Marketing Management (2020-2028) ($MN)
  • Table 23 Global Automated Machine Learning (AutoML) Market Outlook, By Transport Optimization (2020-2028) ($MN)
  • Table 24 Global Automated Machine Learning (AutoML) Market Outlook, By Other Applications (2020-2028) ($MN)
  • Table 25 Global Automated Machine Learning (AutoML) Market Outlook, By End User (2020-2028) ($MN)
  • Table 26 Global Automated Machine Learning (AutoML) Market Outlook, By BFSI (2020-2028) ($MN)
  • Table 27 Global Automated Machine Learning (AutoML) Market Outlook, By Healthcare (2020-2028) ($MN)
  • Table 28 Global Automated Machine Learning (AutoML) Market Outlook, By IT & Telecom (2020-2028) ($MN)
  • Table 29 Global Automated Machine Learning (AutoML) Market Outlook, By Retail (2020-2028) ($MN)
  • Table 30 Global Automated Machine Learning (AutoML) Market Outlook, By E-Commerce (2020-2028) ($MN)
  • Table 31 Global Automated Machine Learning (AutoML) Market Outlook, By Manufacturing (2020-2028) ($MN)
  • Table 32 Global Automated Machine Learning (AutoML) Market Outlook, By Government (2020-2028) ($MN)
  • Table 33 Global Automated Machine Learning (AutoML) Market Outlook, By Other End Users (2020-2028) ($MN)

Note: Tables for North America, Europe, APAC, South America, and Middle East & Africa Regions are also represented in the same manner as above.