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Global Automated Machine Learning Market Outlook to 2028

Region:Global

Author(s):Naman Rohilla

Product Code:KROD1429

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Published On

November 2024

Total pages

85

About the Report

Global Automated Machine Learning Market Overview

  • The Global Automated Machine Learning (AutoML) Market was valued at USD 1 billion in 2023. This market is primarily driven by the increasing demand for automated solutions in data science, the rising need for advanced analytics and artificial intelligence (AI) capabilities, and the growing focus on reducing the complexity and cost associated with traditional machine learning (ML) processes.
  • The major players in the global AutoML market include DataRobot, H2O.ai, Google LLC, Microsoft Corporation, and IBM Corporation. These companies are at the forefront of developing innovative AutoML platforms, focusing on enhancing usability, scalability, and integration with existing enterprise systems.
  • DataRobot's AutoML 2.0 platform, launched in 2023, simplifies AI model deployment with 21% faster results using Quickrun Autopilot, No-Code AI Apps built directly from model leaderboards, and support for datasets up to 5GB for Auto Time Series modelling, enabling end-to-end automation for enterprises.
  • In 2023, North America dominated the global AutoML market due to the high adoption of AI technologies, investments in research and development, and the presence of major technology companies.

Global Automated Machine Learning Market Size

Global Automated Machine Learning Market Segmentation

The global Automated Machine Learning market is segmented by type, application, and region.

  • By Type: The market is segmented into cloud-based and on-premises solutions. In 2023, cloud-based solutions held the highest market share due to the increasing adoption of cloud computing and the scalability offered by cloud platforms.
  • By Application: The market is segmented into finance, healthcare, retail, manufacturing, and others. In 2023, the finance segment held the largest market share, driven by the growing use of AI for risk management, fraud detection, and customer analytics.

Global Automated Machine Learning Market Segmentation by application

  • By Region: The global AutoML market is segmented into North America, Europe, Asia-Pacific (APAC), Middle East & Africa (MEA), and Latin America. In 2023, North America held the largest market share, supported by advanced technological infrastructure and the early adoption of AI technologies.

Global Automated Machine Learning Market Segmentation by region

Global Automated Machine Learning Market Competitive Landscape

Company

Establishment Year

Headquarters

DataRobot

2012

Boston, USA

H2O.ai

2012

Mountain View, USA

Google LLC

1998

Mountain View, USA

Microsoft Corporation

1975

Redmond, USA

IBM Corporation

1911

Armonk, USA

  • H2O.ai: H2O.ai recently released H2O Wave, an open-source platform for building AI applications, and enhanced its Hybrid Cloud offering with improved user interfaces and the AI App Store. The company was also recognized as a leader in the Gartner Magic Quadrant for Data Science and Machine Learning Platforms in 2023.
  • Google LLC: In April 2024, Google announced a major reorganization to streamline AI model development by merging its Google Brain team with DeepMind into a single entity called Google DeepMind. This consolidation aims to accelerate AI innovation.

Global Automated Machine Learning Market Analysis

Global Automated Machine Learning Market Growth Drivers:

  • Increasing Demand for Automated Solutions: The rise of automated data science solutions is evident as organizations seek to streamline AI model deployment. Automating tasks like data preprocessing and model selection allows businesses to deploy AI models 30-50% faster, addressing the critical shortage of data science expertise and enhancing operational efficiency in various sectors.
  • Growing Applications in AI and Analytics: AutoML is transforming industries like healthcare, automating complex ML tasks for disease diagnosis, treatment optimization, and drug discovery. In finance, automated solutions have reduced fraud detection time by up to 40%, while enhancing diagnostic accuracy in healthcare, showcasing its versatility and effectiveness.
  • Reduction in Complexity and Cost: By automating repetitive tasks, AutoML reduces the complexity and time required to build and deploy machine learning models, lowering barriers to AI adoption by around 60%. Cloud-based AutoML solutions offer flexibility and cost-effectiveness, making AI accessible for SMEs without extensive resources.

Global Automated Machine Learning Market Challenges:

  • Data Privacy and Security Concerns: The use of cloud-based AutoML platforms raises data privacy and security concerns, particularly in sensitive sectors like healthcare. Around 70% of healthcare organizations express reluctance to adopt cloud solutions due to fears surrounding patient data privacy, prompting a preference for on-premise frameworks that ensure compliance with strict regulations.
  • Limited Awareness in Emerging Markets: Limited awareness of AutoML's benefits poses a challenge in emerging markets. Studies indicate that over 60% of organizations in these regions lack an understanding of automated machine learning applications, hindering their ability to leverage AutoML for competitive advantage and stalling market growth and innovation.

Global Automated Machine Learning Market Government Initiatives:

  • EU AI Act: The EU AI Act, the worlds first comprehensive AI regulation, aims to ensure the safe and ethical use of AI technologies. It categorizes AI systems by risk level, imposing strict rules on high-risk applications. The Act is expected to be fully operational by 2026, with penalties for non-compliance reaching up to 35 million or 7% of global turnover.
  • US National AI Initiative: The US National AI Initiative promotes the development and adoption of AI technologies across various sectors to maintain global leadership in AI innovation. It emphasizes research funding, workforce development, and international collaboration, with an investment of $1.5 billion in AI research and development planned for 2024.

Global Automated Machine Learning Market Future Market Outlook

The Global Automated Machine Learning Market is expected to grow robustly over the coming years, driven by increasing demand for AI-powered automation, rising applications across various industries, and supportive government initiatives.

Global Automated Machine Learning Market Future Market Trends:

  • Increased Use in Predictive Analytics: By 2028, the use of AutoML in predictive analytics is expected to rise, particularly in finance and healthcare. Research indicates that organizations leveraging AutoML for predictive analytics have improved decision-making efficiency, with companies like PayPal reporting a fraud detection model accuracy increase from 89% to 94.7% after adopting AutoML tools.
  • Advancements in AI Model Explainability: Advancements in AI model explainability are anticipated by 2028, with AutoML platforms expected to enhance transparency and interpretability. Over 60% of data scientists believe improving model explainability is crucial for broader AI adoption, as organizations prioritize ethical AI practices and seek to build trust in automated decision-making systems.

Scope of the Report

By Type

Cloud-Based

On-Premises

By Application

Finance

Healthcare

Retail

Manufacturing

Others

By Region

North America

Europe

Asia-Pacific (APAC)

Middle East & Africa (MEA)

Latin America

Products

Key Target Audience Organizations and Entities Who Can Benefit by Subscribing This Report:

  • Government and Regulatory Bodies
  • Banks and Financial Institutes
  • Investors and Venture Capitalists
  • AI and Data Science Companies
  • Telecommunications Companies
  • Automotive Companies

Time Period Captured in the Report

  • Historical Period: 2018-2023
  • Base Year: 2023
  • Forecast Period: 2023-2028

Companies

  • DataRobot
  • H2O.ai
  • Google LLC
  • Microsoft Corporation
  • IBM Corporation
  • Amazon Web Services (AWS)
  • Alteryx, Inc.
  • RapidMiner
  • TIBCO Software Inc.
  • Databricks
  • BigML, Inc.
  • Peltarion
  • SigOpt
  • Aible
  • dotData

Table of Contents

1. Global Automated Machine Learning Market Overview

1.1. Definition and Scope

1.2. Market Taxonomy

1.3. Market Growth Rate

1.4. Market Segmentation Overview

2. Global Automated Machine Learning Market Size (in USD Billion), 2018-2023

2.1. Historical Market Size

2.2. Year-on-Year Growth Analysis

2.3. Key Market Developments and Milestones

3. Global Automated Machine Learning Market Analysis

3.1. Growth Drivers

3.1.1. Increasing Demand for Automated Solutions

3.1.2. Growing Applications in AI and Analytics

3.1.3. Reduction in Complexity and Cost

3.2. Restraints

3.2.1. Data Privacy and Security Concerns

3.2.2. Limited Awareness in Emerging Markets

3.3. Opportunities

3.3.1. Advancements in AI Model Explainability

3.3.2. Increased Use in Predictive Analytics

3.4. Trends

3.4.1. Increased Focus on Ethical AI Practices

3.4.2. Integration with Cloud Computing Technologies

3.5. Government Regulation

3.5.1. EU AI Act

3.5.2. US National AI Initiative

3.6. SWOT Analysis

3.7. Stakeholder Ecosystem

3.8. Competition Ecosystem

4. Global Automated Machine Learning Market Segmentation, 2023

4.1. By Type (in Value %)

4.1.1. Cloud-Based

4.1.2. On-Premises

4.2. By Application (in Value %)

4.2.1. Finance

4.2.2. Healthcare

4.2.3. Retail

4.2.4. Manufacturing

4.2.5. Others

4.3. By Region (in Value %)

4.3.1. North America

4.3.2. Europe

4.3.3. Asia-Pacific (APAC)

4.3.4. Middle East & Africa (MEA)

4.3.5. Latin America

5. Global Automated Machine Learning Market Cross Comparison

5.1. Detailed Profiles of Major Companies

5.1.1. DataRobot

5.1.2. H2O.ai

5.1.3. Google LLC

5.1.4. Microsoft Corporation

5.1.5. IBM Corporation

5.1.6. Amazon Web Services (AWS)

5.1.7. Alteryx, Inc.

5.1.8. RapidMiner

5.1.9. TIBCO Software Inc.

5.1.10. Databricks

5.1.11. BigML, Inc.

5.1.12. Peltarion

5.1.13. SigOpt

5.1.14. Aible

5.1.15. dotData

5.2. Cross Comparison Parameters (No. of Employees, Headquarters, Inception Year, Revenue)

6. Global Automated Machine Learning Market Competitive Landscape

6.1. Market Share Analysis

6.2. Strategic Initiatives

6.3. Mergers and Acquisitions

6.4. Investment Analysis

6.4.1. Venture Capital Funding

6.4.2. Government Grants

6.4.3. Private Equity Investments

7. Global Automated Machine Learning Market Regulatory Framework

7.1. Environmental Regulations

7.2. Compliance Requirements

7.3. Certification Processes

8. Global Automated Machine Learning Market Future Market Size (in USD Billion), 2023-2028

8.1. Future Market Size Projections

8.2. Key Factors Driving Future Market Growth

9. Global Automated Machine Learning Market Future Market Segmentation, 2028

9.1. By Type (in Value %)

9.2. By Application (in Value %)

9.3. By Region (in Value %)

10. Global Automated Machine Learning Market Analysts Recommendations

10.1. TAM/SAM/SOM Analysis

10.2. Customer Cohort Analysis

10.3. Marketing Initiatives

10.4. White Space Opportunity Analysis

11. Disclaimer

12. Contact Us

Research Methodology

Step: 1 Identifying Key Variables

Ecosystem creation for all the major entities and referring to multiple secondary and proprietary databases to perform desk research around the market to collate market-level information.

Step: 2 Market Building

Collating statistics on the Global Automated Machine Learning market over the years and analyzing the penetration of products as well as the ratio of suppliers to compute the revenue generated for the market. We will also review product quality statistics to ensure accuracy behind the data points shared.

Step: 3 Validating and Finalizing

Building market hypotheses and conducting CATIs with market experts from different companies to validate statistics and seek operational and financial information from company representatives.

Step: 4 Research Output

Our team will approach multiple Automated Machine Learning processing companies and understand the nature of product segments and sales, consumer preference, and other parameters, which will support us in validating statistics derived through the bottom-to-top approach from Automated Machine Learning product manufacturers.

Frequently Asked Questions

01. How big is the Global Automated Machine Learning Market?

The Global Automated Machine Learning Market was valued at USD 1 billion in 2023, driven by the increasing demand for AI-powered automation, the growing use of AutoML in various industries, and the need to reduce the complexity of traditional machine learning processes.

02. Who are the major players in the Global Automated Machine Learning market?

The major players in the global autoML market include DataRobot, H2O.ai, Google LLC, Microsoft Corporation, and IBM Corporation. These companies are leaders in the development of AutoML platforms, focusing on improving usability, scalability, and integration with existing enterprise systems.

03. What are the growth drivers of the Global Automated Machine Learning market?

The growth drivers of the Global AutoML market include the increasing demand for automated data science solutions, the growing applications of AI and analytics across industries, and the need to reduce the complexity and cost associated with traditional machine learning processes.

04. What are the challenges in the Global Automated Machine Learning market?

The Global AutoML market faces challenges such as data privacy and security concerns, particularly with cloud-based solutions, and limited awareness of AutoML benefits and applications in emerging markets, which can hinder market growth.

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