MLaaS(Machine Learning As A Service) : 시장 점유율 분석, 산업 동향, 통계, 성장 예측(2025-2030년)

The Machine Learning As A Service Market size is estimated at USD 45.76 billion in 2025, and is expected to reach USD 209.63 billion by 2030, at a CAGR of 35.58% during the forecast period (2025-2030).

The Machine Learning as a Service market is evolving rapidly owing to the growing adoption of cloud-based services, IoT, and automation in businesses, the growing need among businesses for accelerated time to market for intelligent applications, the rising need to understand consumer behavior coupled with the growing need to enhance decision-making, automate processes, and drive innovation.

Key Highlights

• MLaaS model is poised to dominate the market, with users having the option to choose from a wide variety of tools such as data visualization, APIs, face recognition, natural language processing, predictive analytics, and deep learning focused on different business needs. Advancements in data science and artificial intelligence have propelled the pace of machine learning's performance. Companies are increasingly recognizing the technology's potential, indicating a projected uptick in adoption rates of MLaaS over the forecast period.
• Moreover, MLaaS empowers businesses to leverage the potential of machine learning without the need for extensive in-house expertise, thus making it a valuable tool in fostering innovation and competitive advantage. Further, as businesses worldwide seek to leverage the predictive capabilities of machine learning in real-time scenarios, the demand for MLaaS platforms is analyzed to grow at a rapid pace among businesses to improve decision-making, automate processes, and enhance user experiences.
• Machine learning-as-a-service (MLaaS) is a pivotal feature within cloud computing offerings. With its array of tools, from data visualization and APIs to facial recognition, NLP, predictive analysis, and deep learning, MLaaS stands out as a comprehensive solution for businesses seeking to enhance their operations. The rapid expansion of cloud services and businesses increasingly transitioning to cloud platforms underscores a promising trajectory for MLaaS.
• Machine learning (ML) technology introduces a novel attack surface, garnering significant research attention. As ML integrates further into daily operations, spanning healthcare, finance, mobile devices, automotive systems, and home security, it inevitably becomes a prime target for cyber attackers.
• Post-pandemic, the spending on cloud services across enterprises is witnessing significant growth, which is analyzed to bolster the adoption of MLaaS platforms in the end-user sectors. For instance, according to Flexera Software's State of the Cloud Report 2024, by late 2024, 17% of enterprise respondents reported annual public cloud expenditures ranging from over USD 6 million to USD 12 million. Furthermore, by late 2024, 10% of enterprise respondents reported annual public cloud expenditures of more than USD 60 million. Moreover, 14% of enterprise respondents reported annual public cloud expenditures between USD 12 million and USD 24 million.

Machine Learning As A Service (MLaaS) Market Trends

Healthcare to be the Fastest Growing End User

• The application of machine learning technology has been expanding at a significant pace in the past few years. Healthcare organizations worldwide are demanding machine learning technology to analyze vast amounts of patient data to identify patterns and make more accurate predictions about disease diagnosis, drug discovery, and personalized treatment plans. Due to these factors, the need to access machine learning tools and resources cost-effectively has driven the demand for MLaaS platforms in the healthcare sector.
• The demand for MLaaS is gaining significant traction in healthcare organizations to manage staff schedules effectively. Machine Learning as a Service (MLaaS) equips healthcare organizations with advanced scheduling algorithms. These algorithms are designed to analyze extensive historical data, enabling precise predictions of future staffing requirements. Further, the adoption of MLaaS in healthcare organizations eliminates the need to develop these complex algorithms in-house, saving them time and resources.
• In July 2023, Amazon Web Services Inc. (AWS) unveiled AWS HealthScribe, a HIPAA-eligible service. This service equips healthcare software providers to create clinical applications that leverage speech recognition and generative AI. The goal is to streamline clinicians' workflows by automating the generation of clinical documentation. AWS HealthScribe, backed by Amazon Bedrock, simplifies the integration of generative AI features for healthcare software providers.
• Notably, it offers this functionality for two key medical specialties-general medicine and orthopedics-eliminating the need for providers to handle the complex machine-learning infrastructure or develop their own large language models (LLMs). Such developments further support the market growth.
• By application other applications such as NLP, computer vision, and sentiment analysis are analyzed to gain significant traction in the healthcare sector. For instance, MLaaS platforms offer computer vision capabilities, spotting irregularities in X-rays, CT scans, MRIs, and mammograms, thus helping healthcare providers in diagnosing diseases. Furthermore, MLaaS platforms can also offer sentiment analysis services that can effectively measure patients' emotions, moods, or satisfaction levels.
• Therefore, the adoption of MLaaS platforms in the healthcare sector is analyzed to revolutionize the healthcare sector by helping healthcare providers to effectively diagnose diseases, monitor patients' health, drug discovery, and offer personalized treatment to enhance patient care.
• Additionally, the expanding use of IoT, notably medical IoT devices, and the growing adoption of cloud-based services in healthcare organizations worldwide will further bolster the growth of the MLaaS market in the healthcare sector over the forecast period.
• The increasing adoption of IoT in businesses fuels a heightened need to effectively extract meaningful insights from the vast data generated by IoT devices. This demand is propelling the rapid growth of Machine Learning as a Service (MLaaS), which is increasingly shaping data mining and enabling the creation of innovative business solutions. For instance, according to the data from GSMA, the number of enterprise Internet of Things (IoT) connections worldwide is forecasted to reach 24 billion by 2030.

North America Holds Largest Market Share

• North America is expected to hold a significant share of the market owing to its robust innovation ecosystem, fueled by strategic federal investments into advanced technology and complemented by the presence of visionary scientists and entrepreneurs coming together from globally renowned research institutions, which has propelled the development of MLaaS.
• For instance, in May 2023, The US National Science Foundation (NSF), in collaboration with higher education institutions, other federal agencies, and other stakeholders, announced an investment of USD 140 million to establish seven new National Artificial Intelligence Research Institutes (AI) institutes. Through this investment, the government aims to promote AI systems and technologies and develop a diverse AI workforce in the United States to advance a cohesive approach to AI-related opportunities and risks. Such investments by the regional government are expected to create new growth opportunities for the market studied.
• In addition, in March 2024, Intel announced a significant USD 100 billion investment in an expansion and upgrade initiative. This initiative includes establishing new manufacturing plants in four US states and enhancing current facilities, bolstered by the federal government's financial backing. The US government committed USD 19.5 billion in federal grants and an additional USD 25 billion in tax incentives to bolster Intel's expansion. Furthermore, Intel plans to construct "the world's largest AI chip manufacturing site" near Columbus, Ohio, within the next five years. Such initiatives in AI may further propel the studied market demand in the region.
• The region also witnessed a significant proliferation of 5G, IoT, and connected devices. As a result, communications service providers (CSPs) need to manage an ever-growing complexity efficiently through virtualization, network slicing, new use cases, and service requirements. This is expected to drive MLaaS solutions as traditional network and service management approaches are no longer sustainable. According to GSMA, North America's total number of consumer and industrial IoT connections is forecast to grow to 5.4 billion by 2025.

Machine Learning As A Service (MLaaS) Industry Overview

The MLaaS market is highly fragmented, with the presence of major players like Microsoft Corporation, IBM Corporation, Google LLC (Alphabet Inc.), SAS Institute Inc., and Fair Isaac Corporation (FICO). Players in the market are adopting strategies such as partnerships and acquisitions to enhance their product offerings and gain sustainable competitive advantage.

• May 2024 - Wipro, a prominent technology services and consulting firm, partnered with Microsoft to launch a trio of cognitive assistants tailored for the financial sector. It includes Wipro GenAI Investor Intelligence, Wipro GenAI Investor Onboarding, and Wipro GenAI Loan Origination. The cognitive assistants leveraging Azure OpenAI are designed to merge with current digital and mobile platforms seamlessly. This integration offers a unified and user-friendly information hub for financial professionals and their clientele.
• March 2024 - Hewlett Packard Enterprise unveiled an expansion of its AIOps network management capabilities. This enhancement involves the integration of multiple Generative AI (GenAI) Large Language Models (LLMs) into HPE Aruba Networking Central. This cloud-native network management solution is part of HPE's offerings on the HPE GreenLake Cloud Platform. These enhancements primarily aim to elevate user experience and operational efficiency, with a specific emphasis on search response times, accuracy, and data privacy.

Additional Benefits:

• The market estimate (ME) sheet in Excel format
• 3 months of analyst support

TABLE OF CONTENTS

1 INTRODUCTION

• 1.1 Study Assumptions and Market Definition
• 1.2 Scope of the Study

2 RESEARCH METHODOLOGY

3 EXECUTIVE SUMMARY

4 MARKET INSIGHTS

• 4.1 Market Overview
• 4.2 Industry Attractiveness - Porter's Five Forces Analysis
  • 4.2.1 Bargaining Power of Suppliers
  • 4.2.2 Bargaining Power of Buyers
  • 4.2.3 Threat of New Entrants
  • 4.2.4 Threat of Substitute Products
  • 4.2.5 Intensity of Competitive Rivalry
• 4.3 Industry Value Chain Analysis
• 4.4 Assessment of COVID-19 Impact on the Market

5 MARKET DYNAMICS

• 5.1 Market Drivers
  • 5.1.1 Increasing Adoption of IoT and Automation
  • 5.1.2 Increasing Adoption of Cloud-based Services
• 5.2 Market Restraints
  • 5.2.1 Privacy and Data Security Concerns
  • 5.2.2 Need for Skilled Professionals

6 MARKET SEGMENTATION

• 6.1 By Application
  • 6.1.1 Marketing and Advertisement
  • 6.1.2 Predictive Maintenance
  • 6.1.3 Automated Network Management
  • 6.1.4 Fraud Detection and Risk Analytics
  • 6.1.5 Other Applications
• 6.2 By Organization Size
  • 6.2.1 Small and Medium Enterprises
  • 6.2.2 Large Enterprises
• 6.3 By End User
  • 6.3.1 IT and Telecom
  • 6.3.2 Automotive
  • 6.3.3 Healthcare
  • 6.3.4 Aerospace and Defense
  • 6.3.5 Retail
  • 6.3.6 Government
  • 6.3.7 BFSI
  • 6.3.8 Other End Users
• 6.4 By Geography
  • 6.4.1 North America
  • 6.4.2 Europe
  • 6.4.3 Asia
  • 6.4.4 Australia and New Zealand
  • 6.4.5 Latin America
  • 6.4.6 Middle East and Africa

7 COMPETITIVE LANDSCAPE

• 7.1 Company Profiles
  • 7.1.1 Microsoft Corporation
  • 7.1.2 IBM Corporation
  • 7.1.3 Google LLC (Alphabet Inc.)
  • 7.1.4 SAS Institute Inc.
  • 7.1.5 Fair Isaac Corporation (FICO)
  • 7.1.6 Hewlett Packard Enterprise Company
  • 7.1.7 Yottamine Analytics LLC
  • 7.1.8 Amazon Web Services Inc. (Amazon.Com, Inc.)
  • 7.1.9 BigML Inc.
  • 7.1.10 Iflowsoft Solutions Inc.
  • 7.1.11 Monkeylearn Inc.
  • 7.1.12 Sift Science Inc.
  • 7.1.13 H2O.ai Inc.

8 INVESTMENT ANALYSIS

9 FUTURE OF THE MARKET