세계의 자동 머신러닝 시장 : 자동화 유형, 전개, 용도별 예측(2025-2030년)

The Automated Machine Learning Market was valued at USD 1.63 billion in 2023, expected to reach USD 2.21 billion in 2024, and is projected to grow at a CAGR of 35.70%, to USD 13.88 billion by 2030.

Automated Machine Learning (AutoML) represents a transformative advancement in data science, democratizing access to sophisticated machine learning tools by automating the process of model selection, training, and tuning. The necessity for AutoML arises from the increasing demand for data-driven insights across various sectors such as healthcare, finance, and retail, where traditional machine learning approaches require expert knowledge and substantial time investment. AutoML's applications are extensive, including predictive analytics, anomaly detection, customer segmentation, and more, enhancing decision-making processes across these industries. The end-use scope encompasses businesses of all sizes looking to integrate AI capabilities without necessarily having in-house expertise, offering opportunities for both established enterprises and emerging startups. Market insights indicate that the growth of AutoML is driven by the growing data volume, the rising need for data scientists, and the demand for scalable, efficient AI models. Key opportunities lie in industries facing rapid digital transformation, such as telecommunications and automotive, where AutoML can optimize network operations or autonomous functionalities. However, the market faces limitations such as integration challenges with existing systems and the need for significant initial data preparation. Moreover, there are challenges in ensuring model transparency and interpretability, which are crucial for gaining trust, especially in regulated sectors. Innovations that offer simplified data-preprocessing methods and address transparency issues can significantly propel market growth. Furthermore, investing in user-friendly interfaces and expanding explainable AI capabilities are areas ripe for research and development. The nature of the AutoML market is dynamic, marked by rapid technological advancements and shifting business needs, offering substantial potential for growth. Staying competitive involves continuous innovation and adaptation to emerging AI regulations and ethical standards, ensuring that business strategies align with both technological potential and responsible AI use.

Market Dynamics: Unveiling Key Market Insights in the Rapidly Evolving Automated Machine Learning Market

The Automated Machine Learning Market is undergoing transformative changes driven by a dynamic interplay of supply and demand factors. Understanding these evolving market dynamics prepares business organizations to make informed investment decisions, refine strategic decisions, and seize new opportunities. By gaining a comprehensive view of these trends, business organizations can mitigate various risks across political, geographic, technical, social, and economic domains while also gaining a clearer understanding of consumer behavior and its impact on manufacturing costs and purchasing trends.

• Market Drivers
  • Increasing demand for data-driven insights for decision-making
  • Expanding democratization of machine learning capabilities
• Market Restraints
  • Interpretability and transparency issues associated with AutoML platforms
• Market Opportunities
  • Advancements in artificial intelligence (AI) and machine learning (ML) technologies
  • Growing integration of AutoML with DevOps practices that enhance the development of machine learning models
• Market Challenges
  • Security and privacy concerns of AutoML platforms

Porter's Five Forces: A Strategic Tool for Navigating the Automated Machine Learning Market

Porter's five forces framework is a critical tool for understanding the competitive landscape of the Automated Machine Learning Market. It offers business organizations with a clear methodology for evaluating their competitive positioning and exploring strategic opportunities. This framework helps businesses assess the power dynamics within the market and determine the profitability of new ventures. With these insights, business organizations can leverage their strengths, address weaknesses, and avoid potential challenges, ensuring a more resilient market positioning.

PESTLE Analysis: Navigating External Influences in the Automated Machine Learning Market

External macro-environmental factors play a pivotal role in shaping the performance dynamics of the Automated Machine Learning Market. Political, Economic, Social, Technological, Legal, and Environmental factors analysis provides the necessary information to navigate these influences. By examining PESTLE factors, businesses can better understand potential risks and opportunities. This analysis enables business organizations to anticipate changes in regulations, consumer preferences, and economic trends, ensuring they are prepared to make proactive, forward-thinking decisions.

Market Share Analysis: Understanding the Competitive Landscape in the Automated Machine Learning Market

A detailed market share analysis in the Automated Machine Learning Market provides a comprehensive assessment of vendors' performance. Companies can identify their competitive positioning by comparing key metrics, including revenue, customer base, and growth rates. This analysis highlights market concentration, fragmentation, and trends in consolidation, offering vendors the insights required to make strategic decisions that enhance their position in an increasingly competitive landscape.

FPNV Positioning Matrix: Evaluating Vendors' Performance in the Automated Machine Learning Market

The Forefront, Pathfinder, Niche, Vital (FPNV) Positioning Matrix is a critical tool for evaluating vendors within the Automated Machine Learning Market. This matrix enables business organizations to make well-informed decisions that align with their goals by assessing vendors based on their business strategy and product satisfaction. The four quadrants provide a clear and precise segmentation of vendors, helping users identify the right partners and solutions that best fit their strategic objectives.

Strategy Analysis & Recommendation: Charting a Path to Success in the Automated Machine Learning Market

A strategic analysis of the Automated Machine Learning Market is essential for businesses looking to strengthen their global market presence. By reviewing key resources, capabilities, and performance indicators, business organizations can identify growth opportunities and work toward improvement. This approach helps businesses navigate challenges in the competitive landscape and ensures they are well-positioned to capitalize on newer opportunities and drive long-term success.

Key Company Profiles

The report delves into recent significant developments in the Automated Machine Learning Market, highlighting leading vendors and their innovative profiles. These include Aible, Inc., Akkio Inc., Altair Engineering Inc., Alteryx, Amazon Web Services, Inc., Automated Machine Learning Ltd., BigML, Inc., Databricks, Inc., Dataiku, DataRobot, Inc., Google LLC by Alphabet Inc., H2O.ai, Inc., Hewlett Packard Enterprise Company, InData Labs Group Limited, Intel Corporation, International Business Machines Corporation, Microsoft Corporation, Oracle Corporation, QlikTech International AB, Runai Labs Ltd., Salesforce, Inc., SAS Institute Inc., ServiceNow, Inc., SparkCognition, Inc., STMicroelectronics, Tata Consultancy Services Limited, TAZI AI, Tellius, Inc., Weidmuller Limited, Wolfram, and Yellow.ai.

Market Segmentation & Coverage

This research report categorizes the Automated Machine Learning Market to forecast the revenues and analyze trends in each of the following sub-markets:

• Based on Automation Type, market is studied across Data Processing, Feature Engineering, Modeling, and Visualization.
• Based on Deployment, market is studied across Cloud and On-premises.
• Based on Application, market is studied across Automotive, Transportations, and Logistics, Banking, Financial Services, and Insurance, Government & Defense, Healthcare & Life Sciences, It & Telecommunications, and Media & Entertainment.
• Based on Region, market is studied across Americas, Asia-Pacific, and Europe, Middle East & Africa. The Americas is further studied across Argentina, Brazil, Canada, Mexico, and United States. The United States is further studied across California, Florida, Illinois, New York, Ohio, Pennsylvania, and Texas. The Asia-Pacific is further studied across Australia, China, India, Indonesia, Japan, Malaysia, Philippines, Singapore, South Korea, Taiwan, Thailand, and Vietnam. The Europe, Middle East & Africa is further studied across Denmark, Egypt, Finland, France, Germany, Israel, Italy, Netherlands, Nigeria, Norway, Poland, Qatar, Russia, Saudi Arabia, South Africa, Spain, Sweden, Switzerland, Turkey, United Arab Emirates, and United Kingdom.

The report offers a comprehensive analysis of the market, covering key focus areas:

1. Market Penetration: A detailed review of the current market environment, including extensive data from top industry players, evaluating their market reach and overall influence.

2. Market Development: Identifies growth opportunities in emerging markets and assesses expansion potential in established sectors, providing a strategic roadmap for future growth.

3. Market Diversification: Analyzes recent product launches, untapped geographic regions, major industry advancements, and strategic investments reshaping the market.

4. Competitive Assessment & Intelligence: Provides a thorough analysis of the competitive landscape, examining market share, business strategies, product portfolios, certifications, regulatory approvals, patent trends, and technological advancements of key players.

5. Product Development & Innovation: Highlights cutting-edge technologies, R&D activities, and product innovations expected to drive future market growth.

The report also answers critical questions to aid stakeholders in making informed decisions:

1. What is the current market size, and what is the forecasted growth?

2. Which products, segments, and regions offer the best investment opportunities?

3. What are the key technology trends and regulatory influences shaping the market?

4. How do leading vendors rank in terms of market share and competitive positioning?

5. What revenue sources and strategic opportunities drive vendors' market entry or exit strategies?

Table of Contents

1. Preface

• 1.1. Objectives of the Study
• 1.2. Market Segmentation & Coverage
• 1.3. Years Considered for the Study
• 1.4. Currency & Pricing
• 1.5. Language
• 1.6. Stakeholders

2. Research Methodology

• 2.1. Define: Research Objective
• 2.2. Determine: Research Design
• 2.3. Prepare: Research Instrument
• 2.4. Collect: Data Source
• 2.5. Analyze: Data Interpretation
• 2.6. Formulate: Data Verification
• 2.7. Publish: Research Report
• 2.8. Repeat: Report Update

3. Executive Summary

4. Market Overview

5. Market Insights

• 5.1. Market Dynamics
  • 5.1.1. Drivers
    • 5.1.1.1. Increasing demand for data-driven insights for decision-making
    • 5.1.1.2. Expanding democratization of machine learning capabilities
  • 5.1.2. Restraints
    • 5.1.2.1. Interpretability and transparency issues associated with AutoML platforms
  • 5.1.3. Opportunities
    • 5.1.3.1. Advancements in artificial intelligence (AI) and machine learning (ML) technologies
    • 5.1.3.2. Growing integration of AutoML with DevOps practices that enhance the development of machine learning models
  • 5.1.4. Challenges
    • 5.1.4.1. Security and privacy concerns of AutoML platforms
• 5.2. Market Segmentation Analysis
• 5.3. Porter's Five Forces Analysis
  • 5.3.1. Threat of New Entrants
  • 5.3.2. Threat of Substitutes
  • 5.3.3. Bargaining Power of Customers
  • 5.3.4. Bargaining Power of Suppliers
  • 5.3.5. Industry Rivalry
• 5.4. PESTLE Analysis
  • 5.4.1. Political
  • 5.4.2. Economic
  • 5.4.3. Social
  • 5.4.4. Technological
  • 5.4.5. Legal
  • 5.4.6. Environmental

6. Automated Machine Learning Market, by Automation Type

• 6.1. Introduction
• 6.2. Data Processing
• 6.3. Feature Engineering
• 6.4. Modeling
• 6.5. Visualization

7. Automated Machine Learning Market, by Deployment

• 7.1. Introduction
• 7.2. Cloud
• 7.3. On-premises

8. Automated Machine Learning Market, by Application

• 8.1. Introduction
• 8.2. Automotive, Transportations, and Logistics
• 8.3. Banking, Financial Services, and Insurance
• 8.4. Government & Defense
• 8.5. Healthcare & Life Sciences
• 8.6. It & Telecommunications
• 8.7. Media & Entertainment

9. Americas Automated Machine Learning Market

• 9.1. Introduction
• 9.2. Argentina
• 9.3. Brazil
• 9.4. Canada
• 9.5. Mexico
• 9.6. United States

10. Asia-Pacific Automated Machine Learning Market

• 10.1. Introduction
• 10.2. Australia
• 10.3. China
• 10.4. India
• 10.5. Indonesia
• 10.6. Japan
• 10.7. Malaysia
• 10.8. Philippines
• 10.9. Singapore
• 10.10. South Korea
• 10.11. Taiwan
• 10.12. Thailand
• 10.13. Vietnam

11. Europe, Middle East & Africa Automated Machine Learning Market

• 11.1. Introduction
• 11.2. Denmark
• 11.3. Egypt
• 11.4. Finland
• 11.5. France
• 11.6. Germany
• 11.7. Israel
• 11.8. Italy
• 11.9. Netherlands
• 11.10. Nigeria
• 11.11. Norway
• 11.12. Poland
• 11.13. Qatar
• 11.14. Russia
• 11.15. Saudi Arabia
• 11.16. South Africa
• 11.17. Spain
• 11.18. Sweden
• 11.19. Switzerland
• 11.20. Turkey
• 11.21. United Arab Emirates
• 11.22. United Kingdom

12. Competitive Landscape

• 12.1. Market Share Analysis, 2023
• 12.2. FPNV Positioning Matrix, 2023
• 12.3. Competitive Scenario Analysis
• 12.4. Strategy Analysis & Recommendation

Companies Mentioned

• 1. Aible, Inc.
• 2. Akkio Inc.
• 3. Altair Engineering Inc.
• 4. Alteryx
• 5. Amazon Web Services, Inc.
• 6. Automated Machine Learning Ltd.
• 7. BigML, Inc.
• 8. Databricks, Inc.
• 9. Dataiku
• 10. DataRobot, Inc.
• 11. Google LLC by Alphabet Inc.
• 12. H2O.ai, Inc.
• 13. Hewlett Packard Enterprise Company
• 14. InData Labs Group Limited
• 15. Intel Corporation
• 16. International Business Machines Corporation
• 17. Microsoft Corporation
• 18. Oracle Corporation
• 19. QlikTech International AB
• 20. Runai Labs Ltd.
• 21. Salesforce, Inc.
• 22. SAS Institute Inc.
• 23. ServiceNow, Inc.
• 24. SparkCognition, Inc.
• 25. STMicroelectronics
• 26. Tata Consultancy Services Limited
• 27. TAZI AI
• 28. Tellius, Inc.
• 29. Weidmuller Limited
• 30. Wolfram
• 31. Yellow.ai