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1284265

到 2028 年的自動機器學習 (AutoML) 市場預測 - 按產品（平台和服務）、按部署類型（雲和本地）、按自動化類型、按公司規模、按應用程序、按最終用戶、按地區世界分析

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個工作天內

價格

根據 Stratistics MRC，2022 年全球自動化機器學習 (AutoML) 市場將達到 8.2 億美元，預測期內復合年增長率為 44.8%，預計達到 7.58 美元十億。

自動機器學習 (AutoML) 是將機器學習生命週期中較為複雜和基本的步驟自動化的過程。 這使得在沒有任何理論背景或先前機器學習專業知識的情況下也很容易參與人工智能開發。 這對初學者和高級 AI 從業者都有好處。 用戶可以將數據上傳到訓練算法，讓系統自動為特定問題選擇合適的神經網絡設計。 AutoML 促進了效率、可擴展性和重複錯誤的消除。

根據 O'Reilly 的一項調查，只有 20% 的受訪者表示他們使用自動化機器學習工具，48% 的受訪者表示他們從未聽說過該技術。

市場動態：

驅動程序

用戶友好型機器學習軟件的發展

專家級機器學習知識供不應求。 這也體現在符合條件的申請人數明顯高於職位空缺數。 AutoML 試圖通過自動化非專家無法完成的程序來填補這一空白。 AutoML 是一種用戶友好的機器學習程序，具有易於理解的界面，因此具有基本技術知識的任何人和學習者都可以使用它。

約束

缺乏熟練的專業知識

AutoML 的平台要求用戶具有紮實的編程、數據科學和機器學習背景。 找到創建、實施和管理 AutoML 模型所需的技能對公司來說是一個挑戰。 任何使用 AutoML 平台的人都應該不斷提高自己的技能，並及時了解行業的最新發展。 由於人才短缺，企業面臨來自競爭對手的激烈競爭。 數據管理、數據可視化和雲計算方面的技能短缺阻礙了市場擴張。

機會

提高運營效率並降低成本

機器學習的普及正在幫助公司降低成本。 通過採用 AutoML 解決方案，公司可以降低投資於昂貴的基礎設施和聘請專家的成本。 此外，人工智能解決方案的快速開發和部署提高了運營效率並加強了決策。 機器學習的大眾化使公司能夠擴展其服務範圍並開拓新市場，從而增加利潤和市場份額。

威脅

對 AutoML 的認知度較低

儘管 AutoML 具有提高準確性、可擴展性和效率等諸多優勢，但許多公司仍對引入它猶豫不決。 許多高管和決策者可能不知道 AutoML 的好處及其對您的業務的影響。 自動化機器學習 (AutoML) 解決方案缺乏滲透是市場擴張的主要障礙。

COVID-19 的影響：

在 COVID-19 爆發期間，組織越來越依賴智能解決方案來實現企業流程自動化。 用於識別 COVID-19 實例的技術。 它不僅在病毒藥物的開發方面表現出色，而且在診斷、預後和流行病預測方面也表現出色。 我們開發了許多機器學習 (ML) 模型來估計 COVID-19 存活的可能性，使用自動機器學習 (autoML) 對它們進行比較，並確定最佳模型。 它已經發展成為一種工具，可以幫助醫生在醫院對患者進行分層。

預計在預測期內數據處理部門將是最大的

據估計，數據處理行業將實現有利可圖的增長。 查找和修復數據問題的過程可以通過 autoML 實現自動化。 這包括查找缺失的數字、修復數據格式問題和刪除異常值。 它還包括可以自動應用於您的數據的技術，例如規範化和標準化。 通過將數據轉換成更好的格式，錯誤和差異就不太可能發生。 與手動處理數據相比，它花費的時間和精力更少。

雲部分預計在預測期內表現出最高的複合年增長率。

由於互聯網連接的增加，雲部分預計在預測期內以最快的複合年增長率增長。 基於雲的 AutoML 系統提供了更大的可擴展性和靈活性。 隨著工作負載和數據量的變化，您可以根據需要輕鬆擴大或縮小規模。 此外，計費系統往往採用按量付費的方式，對於工作量波動較大的業務來說更為經濟。 它不需要初始投資，並以合理的價格提供完整的功能。

市場份額最大的地區

預計在預測期內，北美將佔據最大的市場份額。 這極大地促進了自動化機器學習市場的增長和發展。 美國是該地區最發達的國家之一。 AutoML 行業在美國迅速擴張，多家領先公司提供從全自動平台到幫助數據科學家創建機器學習模型的工具等各種產品和服務。 在美國，AutoML 解決方案的使用顯著增加，尤其是在醫療保健、銀行和零售等行業。

複合年增長率最高的地區：

由於技術不斷進步，預計亞太地區在預測期內的複合年增長率最高。 亞太國家是 IT 外包的首選目的地。 經濟的快速增長、對 IT 基礎設施的投資增加、創新技術的採用增加以及政府為推進 AI 技術所做的更多努力被認為是推動該地區市場增長的因素。。

主要發展：

2023 年 2 月，IBM 將把 StepZen 的技術整合到其產品組合中，為客戶提供構建、連接和管理 API 和數據源的端到端解決方案，以加快創新並從數據中創造更大的價值。

2022 年 11 月，Amazon Web Services, Inc. 開設了第二個設施，即 AWS 亞太地區（海得拉巴）區域，以加強其對印度客戶的服務。 它將成為印度 AWS 供應鏈的一部分，包括該國更廣泛經濟中的建築、設備維護、工程、電信和就業。

2022 年 11 月，微軟將通過 Amazon.in、Reliance Digital、Croma、Vijay Sales 和部分多品牌商店在印度推出其新的 Surface 產品 Surface Laptop 5 和 Surface Pro 9 的預購。宣布開始 隨著新 Surface 產品的發布，Microsoft 的精華匯集在一台設備中，每個人都可以參與、被看到、被聽到並表達他們的創造力。

2022 年 10 月，Oracle 將與 NVIDIA 合作，為客戶提供 Nvidia GPU 用於機器學習工作負載的訪問權限，從而增強 Oracle 機器學習工具的性能和功能。

2022 年 6 月，Google LLC 將更名為 Google Public Sector，這是 Google 的一個新部門，專注於幫助美國公共機構（包括聯邦、州、地方政府和教育機構）加速數字化轉型。隨著成立，我們宣布我們將擴大在美國的努力。

本報告內容

  • 按地區和國家/地區劃分的市場份額評估
  • 向新進入者提出戰略建議
  • 2020、2021、2022、2025 和 2028 年的綜合市場數據
  • 市場趨勢（驅動因素、制約因素、機會、威脅、挑戰、投資機會、建議）
  • 根據市場預測在關鍵業務領域提出戰略建議
  • 競爭格局映射關鍵共同趨勢。
  • 公司簡介，包括詳細的戰略、財務狀況和近期發展
  • 映射最新技術進步的供應鏈趨勢

免費定制優惠：

購買此報告的客戶將獲得以下免費定制選項之一：

  • 公司簡介
    • 其他市場參與者的綜合概況（最多 3 家公司）
    • 主要參與者的 SWOT 分析（最多 3 家公司）
  • 區域細分
    • 根據客戶的要求對主要國家/地區的市場估計/預測/複合年增長率（注意：基於可行性檢查）。
  • 競爭基準
    • 根據產品組合、區域影響力和戰略聯盟對主要參與者進行基準測試

內容

第 1 章執行摘要

第二章前言

  • 概覽
  • 利益相關者
  • 調查範圍
  • 調查方法
    • 數據挖掘
    • 數據分析
    • 數據驗證
    • 研究方法
  • 調查來源
    • 主要研究來源
    • 二級研究來源
    • 假設

第三章市場趨勢分析

  • 司機
  • 約束因素
  • 機會
  • 威脅
  • 應用分析
  • 最終用戶分析
  • 新興市場
  • COVID-19 的影響

第 4 章波特五力分析

  • 供應商的議價能力
  • 買家的議價能力
  • 替代品的威脅
  • 新進入者的威脅
  • 競爭公司之間的敵對關係

第 5 章全球自動機器學習 (AutoML) 市場：按產品分類

  • 平台
  • 服務

第 6 章全球自動機器學習 (AutoML) 市場：按部署類型

  • 雲
  • 本地

第 7 章全球自動化機器學習 (AutoML) 市場：按自動化類型

  • 特徵工程
  • 信息處理
  • 優化和調整超參數
  • 模型合奏
  • 模型選擇
  • 可視化

第 8 章。全球自動機器學習 (AutoML) 市場：按公司規模分類

  • 小型企業
  • 中型公司
  • 大公司

第 9 章。全球自動機器學習 (AutoML) 市場：按應用

  • 欺詐檢測
  • 體檢
  • 銷售和營銷管理
  • 傳輸優化
  • 其他用途

第 10 章全球自動機器學習 (AutoML) 市場：按最終用戶分類

  • BFSI
  • 醫療保健
  • IT 與電信
  • 零售
  • 電子商務
  • 製造業
  • 政府
  • 其他最終用戶

第 11 章全球自動機器學習 (AutoML) 市場：按地區

  • 北美
    • 美國
    • 加拿大
    • 墨西哥
  • 歐洲
    • 德國
    • 英國
    • 意大利
    • 法國
    • 西班牙
    • 其他歐洲
  • 亞太地區
    • 日本
    • 中國
    • 印度
    • 澳大利亞
    • 新西蘭
    • 韓國
    • 其他亞太地區
  • 南美洲
    • 阿根廷
    • 巴西
    • 智利
    • 其他南美洲
  • 中東和非洲
    • 沙特阿拉伯
    • 阿拉伯聯合酋長國
    • 卡塔爾
    • 南非
    • 其他中東和非洲地區

第12章主要發展

  • 合同、夥伴關係、協作和合資企業
  • 收購與合併
  • 新產品發布
  • 業務擴展
  • 其他關鍵策略

第13章公司簡介

  • 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.