머신러닝 운영 시장 : 구성요소, 배포, 조직 규모, 최종 사용자별 세계 예측(2025-2030년)

The Machine Learning Operations Market was valued at USD 3.24 billion in 2023, expected to reach USD 4.41 billion in 2024, and is projected to grow at a CAGR of 36.22%, to USD 28.26 billion by 2030.

Machine Learning Operations (MLOps) is a rapidly emerging discipline within data science that blends the principles of DevOps with machine learning to streamline the machine learning lifecycle. Its necessity stems from the growing complexities of deploying, monitoring, and maintaining machine learning models in production. With the rising implementation of AI across industries like healthcare, finance, and retail, MLOps ensures operational efficiency, reproducibility, and scalability of ML models. MLOps platforms and tools optimize workflows and reduce bottlenecks by automating processes such as data ingestion, model training, validation, and deployment, leading to faster model updates and better performance. The market is primarily fueled by increasing AI adoption in businesses, the necessity for improving model accuracy, and greater demand for scalability aligning with substantial growth in big data and cloud computing. It's projected to gain notably as industries seek to enhance decision-making and predictive capabilities through advanced AI technologies. However, challenges such as integration complexity, high initial costs, and the lack of skilled personnel can impede market growth. Security concerns and compliance issues related to data privacy also linger, presenting barriers to full-scale adoption. Opportunities lie in sectors like automated ML, real-time model monitoring, and the development of frameworks that facilitate seamless integration with existing IT environments. Firms are advised to invest in developing hybrid cloud platforms and enhancing collaboration between data scientists and IT operations to capitalize on MLOps benefits. Innovators should focus on improving open-source solutions and developing robust governance frameworks to drive broader adoption across different industries. The market is competitive yet promises modernization of AI operations, as businesses prioritize agility and efficiency, transforming how advanced analytics deliver insights and foster data-driven decision-making in today's dynamic market landscape.

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

The Machine Learning Operations 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 utilization of machine learning in the manufacturing sector
  • Government initiatives to digitalize and automate end-user sectors to boost productivity
  • Growing focus on standardization of machine learning processes for better management
• Market Restraints
  • Issues associated with data management due to discrepancies
• Market Opportunities
  • Continuous improvements in machine learning operations and development of new solutions
  • New investments in smart factory and smart manufacturing technologies
• Market Challenges
  • Limited availability of skilled and trained professionals

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

Porter's five forces framework is a critical tool for understanding the competitive landscape of the Machine Learning Operations 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 Machine Learning Operations Market

External macro-environmental factors play a pivotal role in shaping the performance dynamics of the Machine Learning Operations 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 Machine Learning Operations Market

A detailed market share analysis in the Machine Learning Operations 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 Machine Learning Operations Market

The Forefront, Pathfinder, Niche, Vital (FPNV) Positioning Matrix is a critical tool for evaluating vendors within the Machine Learning Operations 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 Machine Learning Operations Market

A strategic analysis of the Machine Learning Operations 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 Machine Learning Operations Market, highlighting leading vendors and their innovative profiles. These include Addepto Sp. z o. o., Alibaba Cloud International, Allegro Artificial Intelligence Ltd., Amazon Web Services, Inc., Anyscale, Inc., BigML Inc., Canonical Ltd., Dataiku, DataRobot, Inc., Domino Data Lab, Inc., Gathr Data Inc., Google LLC by Alphabet Inc., Grid Dynamics Holdings, Inc., H2O.ai, Inc., Hewlett Packard Enterprise Company, Iguazio Ltd. by McKinsey & Company, International Business Machines Corporation, Microsoft Corporation, Neal Analytics, Neptune Labs, Inc., Neuro Inc., Oracle Corporation, Runai Labs Ltd., SAP SE, SAS Institute Inc., Tredence Analytics Solutions Pvt. Ltd., understandAI GmbH, Valohai, Virtusa Corporation, and Weights and Biases, Inc..

Market Segmentation & Coverage

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

• Based on Component, market is studied across Services and Software.
• Based on Deployment, market is studied across Cloud and On-Premise.
• Based on Organization Size, market is studied across Large Enterprises and SMEs.
• Based on End-User, market is studied across Aerospace & Defense, Automotive & Transportation, Banking, Financial Services & Insurance, Building, Construction & Real Estate, Consumer Goods & Retail, Education, Energy & Utilities, Government & Public Sector, Healthcare & Life Sciences, Information Technology & Telecommunication, Manufacturing, Media & Entertainment, and Travel & Hospitality.
• 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 utilization of machine learning in the manufacturing sector
    • 5.1.1.2. Government initiatives to digitalize and automate end-user sectors to boost productivity
    • 5.1.1.3. Growing focus on standardization of machine learning processes for better management
  • 5.1.2. Restraints
    • 5.1.2.1. Issues associated with data management due to discrepancies
  • 5.1.3. Opportunities
    • 5.1.3.1. Continuous improvements in machine learning operations and development of new solutions
    • 5.1.3.2. New investments in smart factory and smart manufacturing technologies
  • 5.1.4. Challenges
    • 5.1.4.1. Limited availability of skilled and trained professionals
• 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. Machine Learning Operations Market, by Component

• 6.1. Introduction
• 6.2. Services
• 6.3. Software

7. Machine Learning Operations Market, by Deployment

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

8. Machine Learning Operations Market, by Organization Size

• 8.1. Introduction
• 8.2. Large Enterprises
• 8.3. SMEs

9. Machine Learning Operations Market, by End-User

• 9.1. Introduction
• 9.2. Aerospace & Defense
• 9.3. Automotive & Transportation
• 9.4. Banking, Financial Services & Insurance
• 9.5. Building, Construction & Real Estate
• 9.6. Consumer Goods & Retail
• 9.7. Education
• 9.8. Energy & Utilities
• 9.9. Government & Public Sector
• 9.10. Healthcare & Life Sciences
• 9.11. Information Technology & Telecommunication
• 9.12. Manufacturing
• 9.13. Media & Entertainment
• 9.14. Travel & Hospitality

10. Americas Machine Learning Operations Market

• 10.1. Introduction
• 10.2. Argentina
• 10.3. Brazil
• 10.4. Canada
• 10.5. Mexico
• 10.6. United States

11. Asia-Pacific Machine Learning Operations Market

• 11.1. Introduction
• 11.2. Australia
• 11.3. China
• 11.4. India
• 11.5. Indonesia
• 11.6. Japan
• 11.7. Malaysia
• 11.8. Philippines
• 11.9. Singapore
• 11.10. South Korea
• 11.11. Taiwan
• 11.12. Thailand
• 11.13. Vietnam

12. Europe, Middle East & Africa Machine Learning Operations Market

• 12.1. Introduction
• 12.2. Denmark
• 12.3. Egypt
• 12.4. Finland
• 12.5. France
• 12.6. Germany
• 12.7. Israel
• 12.8. Italy
• 12.9. Netherlands
• 12.10. Nigeria
• 12.11. Norway
• 12.12. Poland
• 12.13. Qatar
• 12.14. Russia
• 12.15. Saudi Arabia
• 12.16. South Africa
• 12.17. Spain
• 12.18. Sweden
• 12.19. Switzerland
• 12.20. Turkey
• 12.21. United Arab Emirates
• 12.22. United Kingdom

13. Competitive Landscape

• 13.1. Market Share Analysis, 2023
• 13.2. FPNV Positioning Matrix, 2023
• 13.3. Competitive Scenario Analysis
• 13.4. Strategy Analysis & Recommendation

Companies Mentioned

• 1. Addepto Sp. z o. o.
• 2. Alibaba Cloud International
• 3. Allegro Artificial Intelligence Ltd.
• 4. Amazon Web Services, Inc.
• 5. Anyscale, Inc.
• 6. BigML Inc.
• 7. Canonical Ltd.
• 8. Dataiku
• 9. DataRobot, Inc.
• 10. Domino Data Lab, Inc.
• 11. Gathr Data Inc.
• 12. Google LLC by Alphabet Inc.
• 13. Grid Dynamics Holdings, Inc.
• 14. H2O.ai, Inc.
• 15. Hewlett Packard Enterprise Company
• 16. Iguazio Ltd. by McKinsey & Company
• 17. International Business Machines Corporation
• 18. Microsoft Corporation
• 19. Neal Analytics
• 20. Neptune Labs, Inc.
• 21. Neuro Inc.
• 22. Oracle Corporation
• 23. Runai Labs Ltd.
• 24. SAP SE
• 25. SAS Institute Inc.
• 26. Tredence Analytics Solutions Pvt. Ltd.
• 27. understandAI GmbH
• 28. Valohai
• 29. Virtusa Corporation
• 30. Weights and Biases, Inc.