2026 Global: Automated Machine Learning Market-Competitive Review (2032) report
Description
The 2026 Global: Automated Machine Learning Market-Competitive Review (2031) report features the global market size and projected growth/decline data for the period 2021 through 2032. The report primarily provides an examination of the business strategies for the ten largest global companies in the market and how their strategies differ.
Perry/Hope Partners' reports provide the most accurate industry forecasts based on our proprietary economic models. Our forecasts project the product market size nationally and by regions for 2021 to 2032 using regression analysis in our modeling. and Perry/Hope is the only market research publisher that utilizes both longitudinal (historical) and vertical (from market section to market division to market class) analysis, since we study every manufactured product in the countries we analyze. The report also provides written analysis on the market definition, market segments, and SWOT analysis (market strengths, weaknesses, opportunities, and threats).
The market study aims at estimating the market size and the growth potential of this market. Topics analyzed within the report include a detailed breakdown of the global markets for automated machine learning market by geography and historical trend. The scope of the report extends to sizing of the automated machine learning market market and global market trends with market data for 2024 as the base year, 2025 and 2026 as the estimate years with projection of CAGR from 2027 to 2032.
The report also features a list of the top ten largest global players in the market. A review of each company includes 1) an estimate of the market share, 2) a listing of the products and/or services in the market, and 3) the features of these products and/or services in the market. The report has a chapter on Comparative Business Strategies for the largest four players. An example of the Comparative Business Strategies analysis would be -- How does Netflix's business strategy to expand its market share in the global online streaming compare to Amazon Prime's business strategy through its video products and services?
The ten market players in this report and a brief synopsis of their participation in the market are:
The Automated Machine Learning (AutoML) market features dominant players leveraging cloud infrastructure, specialized platforms, and enterprise-grade tools to democratize AI model development. Leading companies include DataRobot, Amazon Web Services (AWS), Microsoft, Google, IBM, H2O.ai, Databricks, Dataiku, dotData, and Alteryx, which together drive the sector's projected growth from USD 2.59 billion in 2025 at a 43.90% CAGR. These firms address key challenges like model automation, compliance, and scalability, serving industries from finance to healthcare. DataRobot excels in value-driven AI with automated modeling platforms incorporating data scientists' best practices, offering enterprise suites with EU AI Act-aligned workflows for regulated sectors. AWS dominates via SageMaker and Bedrock, bundling AutoML into subscriptions with open-model catalogs and global data centers.
Hyper-scale providers like AWS, Microsoft Azure, Google Cloud, and IBM Watson integrate AutoML into comprehensive ecosystems, enabling seamless data preparation, training, and deployment. AWS's infrastructure supports real-time ML for recommendations and fraud detection, while Microsoft's Azure Machine Learning provides AutoML, MLOps, and responsible AI tools for image recognition and forecasting. Google advances through Vertex AI and TensorFlow, powering predictive analytics and autonomous systems via DeepMind innovations. IBM emphasizes trustworthy AI with Watson's natural language processing and bias mitigation, targeting healthcare and finance. H2O.ai differentiates with transparent, open-source algorithms for auditability in regulated environments, while Databricks unifies analytics for big data ML pipelines in energy and financial services.
Specialists like Dataiku, dotData, and Alteryx focus on user-friendly automation, bridging non-experts to advanced capabilities. Dataiku streamlines collaborative ML workflows, dotData automates end-to-end pipelines, and Alteryx embeds generative AI for data prep and decision automation. Databricks further empowers with scalable cloud solutions for real-time insights. These companies navigate market fragmentation by combining domain expertise—such as DataRobot's predictive models and H2O.ai's explainability—with hyperscalers' resources, fostering adoption amid rising demands for governance and efficiency in 2025. Their innovations ensure AutoML's enterprise viability across diverse sectors.
Perry/Hope Partners' reports provide the most accurate industry forecasts based on our proprietary economic models. Our forecasts project the product market size nationally and by regions for 2021 to 2032 using regression analysis in our modeling. and Perry/Hope is the only market research publisher that utilizes both longitudinal (historical) and vertical (from market section to market division to market class) analysis, since we study every manufactured product in the countries we analyze. The report also provides written analysis on the market definition, market segments, and SWOT analysis (market strengths, weaknesses, opportunities, and threats).
The market study aims at estimating the market size and the growth potential of this market. Topics analyzed within the report include a detailed breakdown of the global markets for automated machine learning market by geography and historical trend. The scope of the report extends to sizing of the automated machine learning market market and global market trends with market data for 2024 as the base year, 2025 and 2026 as the estimate years with projection of CAGR from 2027 to 2032.
The report also features a list of the top ten largest global players in the market. A review of each company includes 1) an estimate of the market share, 2) a listing of the products and/or services in the market, and 3) the features of these products and/or services in the market. The report has a chapter on Comparative Business Strategies for the largest four players. An example of the Comparative Business Strategies analysis would be -- How does Netflix's business strategy to expand its market share in the global online streaming compare to Amazon Prime's business strategy through its video products and services?
The ten market players in this report and a brief synopsis of their participation in the market are:
The Automated Machine Learning (AutoML) market features dominant players leveraging cloud infrastructure, specialized platforms, and enterprise-grade tools to democratize AI model development. Leading companies include DataRobot, Amazon Web Services (AWS), Microsoft, Google, IBM, H2O.ai, Databricks, Dataiku, dotData, and Alteryx, which together drive the sector's projected growth from USD 2.59 billion in 2025 at a 43.90% CAGR. These firms address key challenges like model automation, compliance, and scalability, serving industries from finance to healthcare. DataRobot excels in value-driven AI with automated modeling platforms incorporating data scientists' best practices, offering enterprise suites with EU AI Act-aligned workflows for regulated sectors. AWS dominates via SageMaker and Bedrock, bundling AutoML into subscriptions with open-model catalogs and global data centers.
Hyper-scale providers like AWS, Microsoft Azure, Google Cloud, and IBM Watson integrate AutoML into comprehensive ecosystems, enabling seamless data preparation, training, and deployment. AWS's infrastructure supports real-time ML for recommendations and fraud detection, while Microsoft's Azure Machine Learning provides AutoML, MLOps, and responsible AI tools for image recognition and forecasting. Google advances through Vertex AI and TensorFlow, powering predictive analytics and autonomous systems via DeepMind innovations. IBM emphasizes trustworthy AI with Watson's natural language processing and bias mitigation, targeting healthcare and finance. H2O.ai differentiates with transparent, open-source algorithms for auditability in regulated environments, while Databricks unifies analytics for big data ML pipelines in energy and financial services.
Specialists like Dataiku, dotData, and Alteryx focus on user-friendly automation, bridging non-experts to advanced capabilities. Dataiku streamlines collaborative ML workflows, dotData automates end-to-end pipelines, and Alteryx embeds generative AI for data prep and decision automation. Databricks further empowers with scalable cloud solutions for real-time insights. These companies navigate market fragmentation by combining domain expertise—such as DataRobot's predictive models and H2O.ai's explainability—with hyperscalers' resources, fostering adoption amid rising demands for governance and efficiency in 2025. Their innovations ensure AutoML's enterprise viability across diverse sectors.
Table of Contents
32 Pages
- 1.0 Scope of Report and Methodology
- 2.0 Market SWOT Analysis and Players
- 2.1 Market Definition
- 2.2 Market Segments
- 2.3 Market Strengths
- 2.4 Market Weaknesses
- 2.5 Market Threats
- 2.6 Market Opportunities
- 2.7 Major Players
- 3.0 Competitive Analysis
- 3.1 Market Player 1
- 3.2 Market Player 2
- 3.3 Market Player 3
- 3.4 Market Player 4
- 3.5 Market Player 5
- 3.6 Market Player 6
- 3.7 Market Player 7
- 3.8 Market Player 8
- 3.9 Market Player 9
- 3.10 Market Player 10
- 4.0 Comparative Business Strategies
- 4.1 Comparative Business Strategies of Player 1 and 2
- 4.2 Comparative Business Strategies of Player 1 and 3
- 4.3 Comparative Business Strategies of Player 1 and 4
- 4.4 Comparative Business Strategies of Player 2 and 3
- 4.5 Comparative Business Strategies of Player 2 and 4
- 4.6 Comparative Business Strategies of Player 3 and 4
- 5.0 Appendix
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