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Global Machine Learning (ML) Platforms Market Research Report 2024

Global Machine Learning (ML) Platforms Market Research Report 2024

Publishing Date : Jan, 2024

License Type :
 

Report Code : 1715598

No of Pages : 90

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Synopsis
The global Machine Learning (ML) Platforms market was valued at US$ 3997.7 million in 2023 and is anticipated to reach US$ 31360 million by 2030, witnessing a CAGR of 33.6% during the forecast period 2024-2030.
North American market for Machine Learning (ML) Platforms is estimated to increase from $ million in 2023 to reach $ million by 2030, at a CAGR of % during the forecast period of 2024 through 2030.
Asia-Pacific market for Machine Learning (ML) Platforms is estimated to increase from $ million in 2023 to reach $ million by 2030, at a CAGR of % during the forecast period of 2024 through 2030.
The global market for Machine Learning (ML) Platforms in Small and Medium Enterprises (SMEs) is estimated to increase from $ million in 2023 to $ million by 2030, at a CAGR of % during the forecast period of 2024 through 2030.
The major global companies of Machine Learning (ML) Platforms include Palantier, MathWorks, Alteryx, SAS, Databricks, TIBCO Software, Dataiku, H2O.ai and IBM, etc. In 2023, the world's top three vendors accounted for approximately % of the revenue.
This report aims to provide a comprehensive presentation of the global market for Machine Learning (ML) Platforms, 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 Machine Learning (ML) Platforms.
Report Scope
The Machine Learning (ML) Platforms market size, estimations, and forecasts are provided in terms of revenue ($ millions), considering 2023 as the base year, with history and forecast data for the period from 2019 to 2030. This report segments the global Machine Learning (ML) Platforms market comprehensively. Regional market sizes, concerning products by Type, by Application, and by players, are also provided.
For a more in-depth understanding of the market, the report provides profiles of the competitive landscape, key competitors, and their respective market ranks. The report also discusses technological trends and new product developments.
The report will help the Machine Learning (ML) Platforms companies, new entrants, and industry chain related companies in this market with information on the revenues, sales volume, and average price for the overall market and the sub-segments across the different segments, by company, by Type, by Application, and by regions.
Market Segmentation
By Company
Palantier
MathWorks
Alteryx
SAS
Databricks
TIBCO Software
Dataiku
H2O.ai
IBM
Microsoft
Google
KNIME
DataRobot
RapidMiner
Anaconda
Domino
Altair
Segment by Type
Cloud-based
On-premises
Segment by Application
Small and Medium Enterprises (SMEs)
Large Enterprises
By Region
North America
United States
Canada
Europe
Germany
France
UK
Italy
Russia
Nordic Countries
Rest of Europe
Asia-Pacific
China
Japan
South Korea
Southeast Asia
India
Australia
Rest of Asia
Latin America
Mexico
Brazil
Rest of Latin America
Middle East & Africa
Turkey
Saudi Arabia
UAE
Rest of MEA
Chapter Outline
Chapter 1: Introduces the report scope of the report, executive summary of different market segments (by Type, by Application, etc), including the market size of each market segment, future development potential, and so on. It offers a high-level view of the current state of the market and its likely evolution in the short to mid-term, and long term.
Chapter 2: Introduces executive summary of global market size, regional market size, this section also introduces the market dynamics, latest developments of the market, the driving factors and restrictive factors of the market, the challenges and risks faced by companies in the industry, and the analysis of relevant policies in the industry.
Chapter 3: Detailed analysis of Machine Learning (ML) Platforms companies’ competitive landscape, revenue market share, latest development plan, merger, and acquisition information, etc.
Chapter 4: 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 5: 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 6, 7, 8, 9, 10: North America, Europe, Asia Pacific, Latin America, Middle East and Africa segment by country. It provides a quantitative analysis of the market size and development potential of each region and its main countries and introduces the market development, future development prospects, market space, and capacity of each country in the world.
Chapter 11: Provides profiles of key players, introducing the basic situation of the main companies in the market in detail, including product sales, revenue, price, gross margin, product introduction, recent development, etc.
Chapter 12: The main points and conclusions of the report.
Index
1 Report Overview
1.1 Study Scope
1.2 Market Analysis by Type
1.2.1 Global Machine Learning (ML) Platforms Market Size Growth Rate by Type: 2019 VS 2023 VS 2030
1.2.2 Cloud-based
1.2.3 On-premises
1.3 Market by Application
1.3.1 Global Machine Learning (ML) Platforms Market Growth by Application: 2019 VS 2023 VS 2030
1.3.2 Small and Medium Enterprises (SMEs)
1.3.3 Large Enterprises
1.4 Study Objectives
1.5 Years Considered
1.6 Years Considered
2 Global Growth Trends
2.1 Global Machine Learning (ML) Platforms Market Perspective (2019-2030)
2.2 Machine Learning (ML) Platforms Growth Trends by Region
2.2.1 Global Machine Learning (ML) Platforms Market Size by Region: 2019 VS 2023 VS 2030
2.2.2 Machine Learning (ML) Platforms Historic Market Size by Region (2019-2024)
2.2.3 Machine Learning (ML) Platforms Forecasted Market Size by Region (2025-2030)
2.3 Machine Learning (ML) Platforms Market Dynamics
2.3.1 Machine Learning (ML) Platforms Industry Trends
2.3.2 Machine Learning (ML) Platforms Market Drivers
2.3.3 Machine Learning (ML) Platforms Market Challenges
2.3.4 Machine Learning (ML) Platforms Market Restraints
3 Competition Landscape by Key Players
3.1 Global Top Machine Learning (ML) Platforms Players by Revenue
3.1.1 Global Top Machine Learning (ML) Platforms Players by Revenue (2019-2024)
3.1.2 Global Machine Learning (ML) Platforms Revenue Market Share by Players (2019-2024)
3.2 Global Machine Learning (ML) Platforms Market Share by Company Type (Tier 1, Tier 2, and Tier 3)
3.3 Players Covered: Ranking by Machine Learning (ML) Platforms Revenue
3.4 Global Machine Learning (ML) Platforms Market Concentration Ratio
3.4.1 Global Machine Learning (ML) Platforms Market Concentration Ratio (CR5 and HHI)
3.4.2 Global Top 10 and Top 5 Companies by Machine Learning (ML) Platforms Revenue in 2023
3.5 Machine Learning (ML) Platforms Key Players Head office and Area Served
3.6 Key Players Machine Learning (ML) Platforms Product Solution and Service
3.7 Date of Enter into Machine Learning (ML) Platforms Market
3.8 Mergers & Acquisitions, Expansion Plans
4 Machine Learning (ML) Platforms Breakdown Data by Type
4.1 Global Machine Learning (ML) Platforms Historic Market Size by Type (2019-2024)
4.2 Global Machine Learning (ML) Platforms Forecasted Market Size by Type (2025-2030)
5 Machine Learning (ML) Platforms Breakdown Data by Application
5.1 Global Machine Learning (ML) Platforms Historic Market Size by Application (2019-2024)
5.2 Global Machine Learning (ML) Platforms Forecasted Market Size by Application (2025-2030)
6 North America
6.1 North America Machine Learning (ML) Platforms Market Size (2019-2030)
6.2 North America Machine Learning (ML) Platforms Market Growth Rate by Country: 2019 VS 2023 VS 2030
6.3 North America Machine Learning (ML) Platforms Market Size by Country (2019-2024)
6.4 North America Machine Learning (ML) Platforms Market Size by Country (2025-2030)
6.5 United States
6.6 Canada
7 Europe
7.1 Europe Machine Learning (ML) Platforms Market Size (2019-2030)
7.2 Europe Machine Learning (ML) Platforms Market Growth Rate by Country: 2019 VS 2023 VS 2030
7.3 Europe Machine Learning (ML) Platforms Market Size by Country (2019-2024)
7.4 Europe Machine Learning (ML) Platforms Market Size by Country (2025-2030)
7.5 Germany
7.6 France
7.7 U.K.
7.8 Italy
7.9 Russia
7.10 Nordic Countries
8 Asia-Pacific
8.1 Asia-Pacific Machine Learning (ML) Platforms Market Size (2019-2030)
8.2 Asia-Pacific Machine Learning (ML) Platforms Market Growth Rate by Region: 2019 VS 2023 VS 2030
8.3 Asia-Pacific Machine Learning (ML) Platforms Market Size by Region (2019-2024)
8.4 Asia-Pacific Machine Learning (ML) Platforms Market Size by Region (2025-2030)
8.5 China
8.6 Japan
8.7 South Korea
8.8 Southeast Asia
8.9 India
8.10 Australia
9 Latin America
9.1 Latin America Machine Learning (ML) Platforms Market Size (2019-2030)
9.2 Latin America Machine Learning (ML) Platforms Market Growth Rate by Country: 2019 VS 2023 VS 2030
9.3 Latin America Machine Learning (ML) Platforms Market Size by Country (2019-2024)
9.4 Latin America Machine Learning (ML) Platforms Market Size by Country (2025-2030)
9.5 Mexico
9.6 Brazil
10 Middle East & Africa
10.1 Middle East & Africa Machine Learning (ML) Platforms Market Size (2019-2030)
10.2 Middle East & Africa Machine Learning (ML) Platforms Market Growth Rate by Country: 2019 VS 2023 VS 2030
10.3 Middle East & Africa Machine Learning (ML) Platforms Market Size by Country (2019-2024)
10.4 Middle East & Africa Machine Learning (ML) Platforms Market Size by Country (2025-2030)
10.5 Turkey
10.6 Saudi Arabia
10.7 UAE
11 Key Players Profiles
11.1 Palantier
11.1.1 Palantier Company Detail
11.1.2 Palantier Business Overview
11.1.3 Palantier Machine Learning (ML) Platforms Introduction
11.1.4 Palantier Revenue in Machine Learning (ML) Platforms Business (2019-2024)
11.1.5 Palantier Recent Development
11.2 MathWorks
11.2.1 MathWorks Company Detail
11.2.2 MathWorks Business Overview
11.2.3 MathWorks Machine Learning (ML) Platforms Introduction
11.2.4 MathWorks Revenue in Machine Learning (ML) Platforms Business (2019-2024)
11.2.5 MathWorks Recent Development
11.3 Alteryx
11.3.1 Alteryx Company Detail
11.3.2 Alteryx Business Overview
11.3.3 Alteryx Machine Learning (ML) Platforms Introduction
11.3.4 Alteryx Revenue in Machine Learning (ML) Platforms Business (2019-2024)
11.3.5 Alteryx Recent Development
11.4 SAS
11.4.1 SAS Company Detail
11.4.2 SAS Business Overview
11.4.3 SAS Machine Learning (ML) Platforms Introduction
11.4.4 SAS Revenue in Machine Learning (ML) Platforms Business (2019-2024)
11.4.5 SAS Recent Development
11.5 Databricks
11.5.1 Databricks Company Detail
11.5.2 Databricks Business Overview
11.5.3 Databricks Machine Learning (ML) Platforms Introduction
11.5.4 Databricks Revenue in Machine Learning (ML) Platforms Business (2019-2024)
11.5.5 Databricks Recent Development
11.6 TIBCO Software
11.6.1 TIBCO Software Company Detail
11.6.2 TIBCO Software Business Overview
11.6.3 TIBCO Software Machine Learning (ML) Platforms Introduction
11.6.4 TIBCO Software Revenue in Machine Learning (ML) Platforms Business (2019-2024)
11.6.5 TIBCO Software Recent Development
11.7 Dataiku
11.7.1 Dataiku Company Detail
11.7.2 Dataiku Business Overview
11.7.3 Dataiku Machine Learning (ML) Platforms Introduction
11.7.4 Dataiku Revenue in Machine Learning (ML) Platforms Business (2019-2024)
11.7.5 Dataiku Recent Development
11.8 H2O.ai
11.8.1 H2O.ai Company Detail
11.8.2 H2O.ai Business Overview
11.8.3 H2O.ai Machine Learning (ML) Platforms Introduction
11.8.4 H2O.ai Revenue in Machine Learning (ML) Platforms Business (2019-2024)
11.8.5 H2O.ai Recent Development
11.9 IBM
11.9.1 IBM Company Detail
11.9.2 IBM Business Overview
11.9.3 IBM Machine Learning (ML) Platforms Introduction
11.9.4 IBM Revenue in Machine Learning (ML) Platforms Business (2019-2024)
11.9.5 IBM Recent Development
11.10 Microsoft
11.10.1 Microsoft Company Detail
11.10.2 Microsoft Business Overview
11.10.3 Microsoft Machine Learning (ML) Platforms Introduction
11.10.4 Microsoft Revenue in Machine Learning (ML) Platforms Business (2019-2024)
11.10.5 Microsoft Recent Development
11.11 Google
11.11.1 Google Company Detail
11.11.2 Google Business Overview
11.11.3 Google Machine Learning (ML) Platforms Introduction
11.11.4 Google Revenue in Machine Learning (ML) Platforms Business (2019-2024)
11.11.5 Google Recent Development
11.12 KNIME
11.12.1 KNIME Company Detail
11.12.2 KNIME Business Overview
11.12.3 KNIME Machine Learning (ML) Platforms Introduction
11.12.4 KNIME Revenue in Machine Learning (ML) Platforms Business (2019-2024)
11.12.5 KNIME Recent Development
11.13 DataRobot
11.13.1 DataRobot Company Detail
11.13.2 DataRobot Business Overview
11.13.3 DataRobot Machine Learning (ML) Platforms Introduction
11.13.4 DataRobot Revenue in Machine Learning (ML) Platforms Business (2019-2024)
11.13.5 DataRobot Recent Development
11.14 RapidMiner
11.14.1 RapidMiner Company Detail
11.14.2 RapidMiner Business Overview
11.14.3 RapidMiner Machine Learning (ML) Platforms Introduction
11.14.4 RapidMiner Revenue in Machine Learning (ML) Platforms Business (2019-2024)
11.14.5 RapidMiner Recent Development
11.15 Anaconda
11.15.1 Anaconda Company Detail
11.15.2 Anaconda Business Overview
11.15.3 Anaconda Machine Learning (ML) Platforms Introduction
11.15.4 Anaconda Revenue in Machine Learning (ML) Platforms Business (2019-2024)
11.15.5 Anaconda Recent Development
11.16 Domino
11.16.1 Domino Company Detail
11.16.2 Domino Business Overview
11.16.3 Domino Machine Learning (ML) Platforms Introduction
11.16.4 Domino Revenue in Machine Learning (ML) Platforms Business (2019-2024)
11.16.5 Domino Recent Development
11.17 Altair
11.17.1 Altair Company Detail
11.17.2 Altair Business Overview
11.17.3 Altair Machine Learning (ML) Platforms Introduction
11.17.4 Altair Revenue in Machine Learning (ML) Platforms Business (2019-2024)
11.17.5 Altair Recent Development
12 Analyst's Viewpoints/Conclusions
13 Appendix
13.1 Research Methodology
13.1.1 Methodology/Research Approach
13.1.2 Data Source
13.2 Disclaimer
13.3 Author Details

Published By : QY Research

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