Automated Machine Learning (AutoML) - Global Market Share and Ranking, Overall Sales and Demand Forecast 2024-2030

Publication ID:

PB510242245

Publication Date:

October 17, 2024

Pages:

105

Publisher:

MRRPB5

Countries:

Global [1]

USD 3,950.00

IT Services [2]

Computing & Electronics [3]

Description:

The global market for Automated Machine Learning (AutoML) was estimated to be worth US$ 54 million in 2023 and is forecast to a readjusted size of US$ 71 million by 2030 with a CAGR of 4.2% during the forecast period 2024-2030
The Automated Machine Learning (AutoML) market is experiencing significant growth and is expected to continue its upward trend in the coming years. AutoML is a technology that automates the process of building and deploying machine learning models, making it accessible to a wider range of users.One of the main drivers of the AutoML market is the increasing demand for machine learning solutions across various industries. As businesses realize the potential of leveraging machine learning for insights and decision-making, there is a growing need for accessible and efficient tools to develop and deploy these models. AutoML fills this gap by simplifying the complex process of model building and reducing the time and resources required.
Report Scope
This report aims to provide a comprehensive presentation of the global market for Automated Machine Learning (AutoML), focusing on the total sales revenue, key companies market share and ranking, together with an analysis of Automated Machine Learning (AutoML) by region & country, by Type, and by Application.
The Automated Machine Learning (AutoML) market size, estimations, and forecasts are provided in terms of sales revenue ($ millions), considering 2023 as the base year, with history and forecast data for the period from 2019 to 2030. With both quantitative and qualitative analysis, to help readers develop business/growth strategies, assess the market competitive situation, analyze their position in the current marketplace, and make informed business decisions regarding Automated Machine Learning (AutoML).
Market Segmentation
By Company
Amazon Web Services Inc.
DataRobot
EdgeVerve Systems Limited
H20.ai Inc.
IBM
JADBio - Gnosis DA S.A.
QlikTech International AB
Auger
Google
Microsoft
SAS Institute lnc.
Segment by Type:
Platform
Service
Segment by Application
Large Enterprise
SMEs
By Region
North America
United States
Canada
Europe
Germany
France
U.K.
Italy
Russia
Asia-Pacific
China
Japan
South Korea
China Taiwan
Southeast Asia
India
Latin America
Mexico
Brazil
Argentina
Middle East & Africa
Turkey
Saudi Arabia
UAE
Chapter Outline
Chapter 1: Introduces the report scope of the report, global total market size. This chapter also provides the market dynamics, latest developments of the market, the driving factors and restrictive factors of the market, the challenges and risks faced by manufacturers in the industry, and the analysis of relevant policies in the industry.
Chapter 2: Detailed analysis of Automated Machine Learning (AutoML) manufacturers competitive landscape, revenue market share, latest development plan, merger, and acquisition information, etc.
Chapter 3: Provides the analysis of various market segments by Type, covering the market size and development potential of each market segment, to help readers find the blue ocean market in different market segments.
Chapter 4: Provides the analysis of various market segments by Application, covering the market size and development potential of each market segment, to help readers find the blue ocean market in different downstream markets.
Chapter 5: Revenue of Automated Machine Learning (AutoML) in regional level. It provides a quantitative analysis of the market size and development potential of each region and introduces the market development, future development prospects, market space, and market size of each country in the world.
Chapter 6: Revenue of Automated Machine Learning (AutoML) in country level. It provides sigmate data by Type, and by Application for each country/region.
Chapter 7: Provides profiles of key players, introducing the basic situation of the main companies in the market in detail, including product revenue, gross margin, product introduction, recent development, etc.
Chapter 8: Analysis of industrial chain, including the upstream and downstream of the industry.
Chapter 9: Conclusion.

Please Note - This is an on demand report and will be delivered in 2 business days (48 hours) post payment.

Table Of Contents:

1 Market Overview
1.1 Automated Machine Learning (AutoML) Product Introduction
1.2 Global Automated Machine Learning (AutoML) Market Size Forecast
1.3 Automated Machine Learning (AutoML) Market Trends & Drivers
1.3.1 Automated Machine Learning (AutoML) Industry Trends
1.3.2 Automated Machine Learning (AutoML) Market Drivers & Opportunity
1.3.3 Automated Machine Learning (AutoML) Market Challenges
1.3.4 Automated Machine Learning (AutoML) Market Restraints
1.4 Assumptions and Limitations
1.5 Study Objectives
1.6 Years Considered
2 Competitive Analysis by Company
2.1 Global Automated Machine Learning (AutoML) Players Revenue Ranking (2023)
2.2 Global Automated Machine Learning (AutoML) Revenue by Company (2019-2024)
2.3 Key Companies Automated Machine Learning (AutoML) Manufacturing Base Distribution and Headquarters
2.4 Key Companies Automated Machine Learning (AutoML) Product Offered
2.5 Key Companies Time to Begin Mass Production of Automated Machine Learning (AutoML)
2.6 Automated Machine Learning (AutoML) Market Competitive Analysis
2.6.1 Automated Machine Learning (AutoML) Market Concentration Rate (2019-2024)
2.6.2 Global 5 and 10 Largest Companies by Automated Machine Learning (AutoML) Revenue in 2023
2.6.3 Global Top Companies by Company Type (Tier 1, Tier 2, and Tier 3) & (based on the Revenue in Automated Machine Learning (AutoML) as of 2023)
2.7 Mergers & Acquisitions, Expansion
3 Segmentation by Type
3.1 Introduction by Type
3.1.1 Platform
3.1.2 Service
3.2 Global Automated Machine Learning (AutoML) Sales Value by Type
3.2.1 Global Automated Machine Learning (AutoML) Sales Value by Type (2019 VS 2023 VS 2030)
3.2.2 Global Automated Machine Learning (AutoML) Sales Value, by Type (2019-2030)
3.2.3 Global Automated Machine Learning (AutoML) Sales Value, by Type (%) (2019-2030)
4 Segmentation by Application
4.1 Introduction by Application
4.1.1 Large Enterprise
4.1.2 SMEs
4.2 Global Automated Machine Learning (AutoML) Sales Value by Application
4.2.1 Global Automated Machine Learning (AutoML) Sales Value by Application (2019 VS 2023 VS 2030)
4.2.2 Global Automated Machine Learning (AutoML) Sales Value, by Application (2019-2030)
4.2.3 Global Automated Machine Learning (AutoML) Sales Value, by Application (%) (2019-2030)
5 Segmentation by Region
5.1 Global Automated Machine Learning (AutoML) Sales Value by Region
5.1.1 Global Automated Machine Learning (AutoML) Sales Value by Region: 2019 VS 2023 VS 2030
5.1.2 Global Automated Machine Learning (AutoML) Sales Value by Region (2019-2024)
5.1.3 Global Automated Machine Learning (AutoML) Sales Value by Region (2025-2030)
5.1.4 Global Automated Machine Learning (AutoML) Sales Value by Region (%), (2019-2030)
5.2 North America
5.2.1 North America Automated Machine Learning (AutoML) Sales Value, 2019-2030
5.2.2 North America Automated Machine Learning (AutoML) Sales Value by Country (%), 2023 VS 2030
5.3 Europe
5.3.1 Europe Automated Machine Learning (AutoML) Sales Value, 2019-2030
5.3.2 Europe Automated Machine Learning (AutoML) Sales Value by Country (%), 2023 VS 2030
5.4 Asia Pacific
5.4.1 Asia Pacific Automated Machine Learning (AutoML) Sales Value, 2019-2030
5.4.2 Asia Pacific Automated Machine Learning (AutoML) Sales Value by Country (%), 2023 VS 2030
5.5 South America
5.5.1 South America Automated Machine Learning (AutoML) Sales Value, 2019-2030
5.5.2 South America Automated Machine Learning (AutoML) Sales Value by Country (%), 2023 VS 2030
5.6 Middle East & Africa
5.6.1 Middle East & Africa Automated Machine Learning (AutoML) Sales Value, 2019-2030
5.6.2 Middle East & Africa Automated Machine Learning (AutoML) Sales Value by Country (%), 2023 VS 2030
6 Segmentation by Key Countries/Regions
6.1 Key Countries/Regions Automated Machine Learning (AutoML) Sales Value Growth Trends, 2019 VS 2023 VS 2030
6.2 Key Countries/Regions Automated Machine Learning (AutoML) Sales Value
6.3 United States
6.3.1 United States Automated Machine Learning (AutoML) Sales Value, 2019-2030
6.3.2 United States Automated Machine Learning (AutoML) Sales Value by Type (%), 2023 VS 2030
6.3.3 United States Automated Machine Learning (AutoML) Sales Value by Application, 2023 VS 2030
6.4 Europe
6.4.1 Europe Automated Machine Learning (AutoML) Sales Value, 2019-2030
6.4.2 Europe Automated Machine Learning (AutoML) Sales Value by Type (%), 2023 VS 2030
6.4.3 Europe Automated Machine Learning (AutoML) Sales Value by Application, 2023 VS 2030
6.5 China
6.5.1 China Automated Machine Learning (AutoML) Sales Value, 2019-2030
6.5.2 China Automated Machine Learning (AutoML) Sales Value by Type (%), 2023 VS 2030
6.5.3 China Automated Machine Learning (AutoML) Sales Value by Application, 2023 VS 2030
6.6 Japan
6.6.1 Japan Automated Machine Learning (AutoML) Sales Value, 2019-2030
6.6.2 Japan Automated Machine Learning (AutoML) Sales Value by Type (%), 2023 VS 2030
6.6.3 Japan Automated Machine Learning (AutoML) Sales Value by Application, 2023 VS 2030
6.7 South Korea
6.7.1 South Korea Automated Machine Learning (AutoML) Sales Value, 2019-2030
6.7.2 South Korea Automated Machine Learning (AutoML) Sales Value by Type (%), 2023 VS 2030
6.7.3 South Korea Automated Machine Learning (AutoML) Sales Value by Application, 2023 VS 2030
6.8 Southeast Asia
6.8.1 Southeast Asia Automated Machine Learning (AutoML) Sales Value, 2019-2030
6.8.2 Southeast Asia Automated Machine Learning (AutoML) Sales Value by Type (%), 2023 VS 2030
6.8.3 Southeast Asia Automated Machine Learning (AutoML) Sales Value by Application, 2023 VS 2030
6.9 India
6.9.1 India Automated Machine Learning (AutoML) Sales Value, 2019-2030
6.9.2 India Automated Machine Learning (AutoML) Sales Value by Type (%), 2023 VS 2030
6.9.3 India Automated Machine Learning (AutoML) Sales Value by Application, 2023 VS 2030
7 Company Profiles
7.1 Amazon Web Services Inc.
7.1.1 Amazon Web Services Inc. Profile
7.1.2 Amazon Web Services Inc. Main Business
7.1.3 Amazon Web Services Inc. Automated Machine Learning (AutoML) Products, Services and Solutions
7.1.4 Amazon Web Services Inc. Automated Machine Learning (AutoML) Revenue (US$ Million) & (2019-2024)
7.1.5 Amazon Web Services Inc. Recent Developments
7.2 DataRobot
7.2.1 DataRobot Profile
7.2.2 DataRobot Main Business
7.2.3 DataRobot Automated Machine Learning (AutoML) Products, Services and Solutions
7.2.4 DataRobot Automated Machine Learning (AutoML) Revenue (US$ Million) & (2019-2024)
7.2.5 DataRobot Recent Developments
7.3 EdgeVerve Systems Limited
7.3.1 EdgeVerve Systems Limited Profile
7.3.2 EdgeVerve Systems Limited Main Business
7.3.3 EdgeVerve Systems Limited Automated Machine Learning (AutoML) Products, Services and Solutions
7.3.4 EdgeVerve Systems Limited Automated Machine Learning (AutoML) Revenue (US$ Million) & (2019-2024)
7.3.5 H20.ai Inc. Recent Developments
7.4 H20.ai Inc.
7.4.1 H20.ai Inc. Profile
7.4.2 H20.ai Inc. Main Business
7.4.3 H20.ai Inc. Automated Machine Learning (AutoML) Products, Services and Solutions
7.4.4 H20.ai Inc. Automated Machine Learning (AutoML) Revenue (US$ Million) & (2019-2024)
7.4.5 H20.ai Inc. Recent Developments
7.5 IBM
7.5.1 IBM Profile
7.5.2 IBM Main Business
7.5.3 IBM Automated Machine Learning (AutoML) Products, Services and Solutions
7.5.4 IBM Automated Machine Learning (AutoML) Revenue (US$ Million) & (2019-2024)
7.5.5 IBM Recent Developments
7.6 JADBio - Gnosis DA S.A.
7.6.1 JADBio - Gnosis DA S.A. Profile
7.6.2 JADBio - Gnosis DA S.A. Main Business
7.6.3 JADBio - Gnosis DA S.A. Automated Machine Learning (AutoML) Products, Services and Solutions
7.6.4 JADBio - Gnosis DA S.A. Automated Machine Learning (AutoML) Revenue (US$ Million) & (2019-2024)
7.6.5 JADBio - Gnosis DA S.A. Recent Developments
7.7 QlikTech International AB
7.7.1 QlikTech International AB Profile
7.7.2 QlikTech International AB Main Business
7.7.3 QlikTech International AB Automated Machine Learning (AutoML) Products, Services and Solutions
7.7.4 QlikTech International AB Automated Machine Learning (AutoML) Revenue (US$ Million) & (2019-2024)
7.7.5 QlikTech International AB Recent Developments
7.8 Auger
7.8.1 Auger Profile
7.8.2 Auger Main Business
7.8.3 Auger Automated Machine Learning (AutoML) Products, Services and Solutions
7.8.4 Auger Automated Machine Learning (AutoML) Revenue (US$ Million) & (2019-2024)
7.8.5 Auger Recent Developments
7.9 Google
7.9.1 Google Profile
7.9.2 Google Main Business
7.9.3 Google Automated Machine Learning (AutoML) Products, Services and Solutions
7.9.4 Google Automated Machine Learning (AutoML) Revenue (US$ Million) & (2019-2024)
7.9.5 Google Recent Developments
7.10 Microsoft
7.10.1 Microsoft Profile
7.10.2 Microsoft Main Business
7.10.3 Microsoft Automated Machine Learning (AutoML) Products, Services and Solutions
7.10.4 Microsoft Automated Machine Learning (AutoML) Revenue (US$ Million) & (2019-2024)
7.10.5 Microsoft Recent Developments
7.11 SAS Institute lnc.
7.11.1 SAS Institute lnc. Profile
7.11.2 SAS Institute lnc. Main Business
7.11.3 SAS Institute lnc. Automated Machine Learning (AutoML) Products, Services and Solutions
7.11.4 SAS Institute lnc. Automated Machine Learning (AutoML) Revenue (US$ Million) & (2019-2024)
7.11.5 SAS Institute lnc. Recent Developments
8 Industry Chain Analysis
8.1 Automated Machine Learning (AutoML) Industrial Chain
8.2 Automated Machine Learning (AutoML) Upstream Analysis
8.2.1 Key Raw Materials
8.2.2 Raw Materials Key Suppliers
8.2.3 Manufacturing Cost Structure
8.3 Midstream Analysis
8.4 Downstream Analysis (Customers Analysis)
8.5 Sales Model and Sales Channels
8.5.1 Automated Machine Learning (AutoML) Sales Model
8.5.2 Sales Channel
8.5.3 Automated Machine Learning (AutoML) Distributors
9 Research Findings and Conclusion
10 Appendix
10.1 Research Methodology
10.1.1 Methodology/Research Approach
10.1.2 Data Source
10.2 Author Details
10.3 Disclaimer

Companies Mentioned:

Amazon Web Services Inc.
DataRobot
EdgeVerve Systems Limited
H20.ai Inc.
IBM
JADBio - Gnosis DA S.A.
QlikTech International AB
Auger
Google
Microsoft
SAS Institute lnc.

License Types:

Single User License (PDF)

This license allows for use of a publication by one person.
This person may print out a single copy of the publication.
This person can include information given in the publication in presentations and internal reports by providing full copyright credit to the publisher.
This person cannot share the publication (or any information contained therein) with any other person or persons.
Unless a Enterprise License is purchased, a Single User License must be purchased for every person that wishes to use the publication within the same organization.
Customers who infringe these license terms are liable for a Global license fee.

听

Site License (PDF)*

This license allows for use of a publication by all users within one corporate location, e.g. a regional office.
These users may print out a single copy of the publication.
These users can include information given in the publication in presentations and internal reports by providing full copyright credit to the publisher.
These users cannot share the publication (or any information contained therein) with any other person or persons outside the corporate location for which the publication is purchased.
Unless a Enterprise License is purchased, a Site User License must be purchased for every corporate location by an organization that wishes to use the publication within the same organization.
Customers who infringe these license terms are liable for a Global license fee.

听

Global License (PDF)*

This license allows for use of a publication by unlimited users within the purchasing organization e.g. all employees of a single company.
Each of these people may use the publication on any computer, and may print out the report, but may not share the publication (or any information contained therein) with any other person or persons outside of the organization.
These employees of purchasing organization can include information given in the publication in presentations and internal reports by providing full copyright credit to the publisher.

*If Applicable.

91制片厂