MLaaS(Machine Learning as a Service)的全球市場 - 成長,趨勢,預測(2019年∼2024年)

Machine Learning as a Service (MLaaS) Market - Growth, Trends, COVID-19 Impact, and Forecasts (2022 - 2027)

出版日期: | 出版商: Mordor Intelligence Pvt Ltd | 英文 150 Pages | 商品交期: 2-3個工作天內



  • 全貌
  • 簡介
  • 目錄

全球MLaaS(Machine Learning as a Service:機器學習即服務)市場在2019年∼2024年間,預測將以超越43%的年複合成長率發展。根據資料科學及人工智能技術的發展,機器學習的能力急速提高。許多企業認識到機器學習的可能性,預期預測期間其引進率將上升。

本報告提供全球MLaaS(Machine Learning as a Service)市場調查,市場概要,各用途、組織規模、終端用戶、地區的市場規模的變化與預測,市場成長要素及阻礙因素分析,市場機會,競爭情形,主要企業的簡介等全面性資訊。


第1章 簡介

  • 調查成果
  • 調查的前提條件
  • 調查範圍

第2章 調查方法

第3章 摘要整理

第4章 市場動態

  • 市場概況
  • 成長要素及阻礙因素概要
  • 市場成長要素
    • IoT與自動化的引進增加
    • 雲端基礎服務的引進增加
    • 終端用戶產業上數位化的需求高漲
  • 市場阻礙因素
    • 隱私和資料安全的疑慮
    • 熟練專家的必要性
  • 波特的五力分析
    • 買方議價能力
    • 供給企業談判力
    • 新加入業者的威脅
    • 替代品的威脅
    • 競爭企業間的敵對關係

第5章 MLaaS(Machine Learning as a Service)市場:各市場區隔

  • 各用途
    • 行銷、廣告
    • 預知保全
    • 自動網路管理
    • 詐欺檢測與風險分析
    • 其他的用途
  • 各組織規模
    • 中小企業
    • 大企業
  • 各終端用戶
    • IT、通訊
    • 汽車
    • 醫療
    • 航太、防衛
    • 零售
    • 政府
    • 銀行、金融服務、保險
    • 其他
  • 各地區
    • 北美
    • 歐洲
    • 亞太地區
    • 其他地區

第6章 競爭情形

  • 企業簡介
    • Microsoft Corporation
    • IBM Corporation
    • Google LLC
    • SAS Institute Inc.
    • Fair Isaac Corporation (FICO)
    • Hewlett Packard Enterprise Company
    • Yottamine Analytics LLC
    • Amazon Web Services Inc.
    • BigML Inc.
    • Iflowsoft Solutions Inc.
    • PurePredictive Inc.
    • Sift Science Inc.
    • H2O.ai Inc.

第7章 投資分析

第8章 市場機會及未來趨勢

Product Code: 55039

The Machine Learning-as-a-service (MLaaS) Market was valued at USD 1.60 billion in 2020, and it is expected to reach USD 12.10 billion by 2026, registering a CAGR of 39.86% during the period of 2021-2026. The COVID-19 pandemic caused many organizations to accelerate their migrations to public cloud solutions, since cloud service elasticity can meet unexpected spikes in service demand. Migrations to the cloud helped companies reinvent the way they conduct their businesses in the time of COVID-19. The need for AI services has grown, and many cloud providers offer AIaaS and MLaaS. As a result, the global cloud market recorded significant growth in the healthcare segment in 2020. AI and ML technology is being used considerably to fight COVID-19.

Key Highlights

  • Machine learning (ML), a subfield of artificial intelligence (AI) in its most straightforward description, spans a broad set of algorithms that are used to extract valuable models from raw data and grew out of traditional statistics and analysis. Since it revolves around algorithms, model complexity, and computational complexity, it requires skilled professionals to develop these solutions.
  • With advancements in data science and artificial intelligence, the performance of machine learning accelerated at a rapid pace. Companies are now identifying the potential of this technology, and therefore, the adoption rate of the same is expected to increase over the forecast period. Companies are offering machine learning solutions on a subscription-based model, making it easier for consumers to take advantage of this technology. In addition, it provides flexibility on a pay-as-you-use basis. MLaaS products offered by companies are microservices offered by significant cloud computing firms like Amazon Web Services, Microsoft Azure, and Google Cloud Platform. These solutions typically include pre-built natural language processing (NLP), computer vision, and general machine learning algorithms.
  • The MLaaS model is poised to dominate the market, with users having an option to choose from a wide variety of solutions focused on different business needs. Also, factors, such as the increasing adoption of cloud-based services, IoT, and automation and the growing demand for consumer behavior analysis, are expected to drive the growth of the machine learning-as-a-service market.
  • As the emergence of electronic sensors, connected machines, and equipment in the industry continues, reinforced by the advancements in network connectivity technology, the demand for MLaaS is expected to grow considerably over the forecast period. Furthermore, industries have emerged into big data generators and need a highly efficient supporting system for gaining insight promptly.

Key Market Trends

Increasing Adoption of IoT and Automation to Drive the Market

  • IoT operations ensure that the thousands or more devices run correctly and safely on an enterprise network, and the data that is being collected is both timely and accurate. While the sophisticated back-end analytics engines work on the heavy lifting of processing the stream of data, ensuring the quality of the data is often left to obsolete methodologies. To ensure the rein in sprawling IoT infrastructures, some IoT platform vendors are baking machine learning technology to boost their operations management capabilities.
  • Machine learning could engage in demystifying the hidden patterns in IoT data by analyzing significant volumes of data utilizing sophisticated algorithms. ML inference could supplement or replace manual processes with automated systems utilizing statistically derived actions in critical processes. Solutions built on ML automate the IoT data modeling process, thus, removing the circuitous and labor-intensive activities of model selection, coding, and validation.
  • Small businesses adopting IoT could significantly save on the time-consuming process of machine learning. MLaaS vendors could conduct more queries more quickly, providing more types of analysis to get more actionable information from vast caches of data generated by multiple devices in the IoT network.
  • As enterprises increasingly adopt IoT-based technologies and solutions, more companies leverage machine learning technologies for data analytics. As a result, MLaaS is expected to drive innovation in IoT. According to Ericsson, total IoT connections are poised to increase from 12.4 billion in 2020 to 26.4 billion in 2026, with a CAGR of 13%. Although MLaaS already has the capacity to integrate with various kinds of sensors, MLaaS is poised to a critical role in IoT and automation.

North America is Expected to Hold Largest Market Share

  • The United States has a robust innovation ecosystem, fueled by strategic federal investments into advanced technology, complemented by the presence of visionary scientists and entrepreneurs coming together from across the world, and renowned research institutions, which has propelled the development of MLaaS in the North American region.
  • The region is also witnessing a significant proliferation of 5G, IoT, and connected devices. As a result, Communications Service Providers (CSPs) need to efficiently manage an ever-growing complexity 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.
  • Moreover, major technology firms present in the region, such as Microsoft, Google, Amazon, and IBM, have stepped up as major players in the ML as a Service race. Because each of the companies has sizeable public cloud infrastructure and ML platforms. This allows the companies to make ML as a Service a reality for those looking to use AI for everything ranging from customer service to robotic process automation, marketing, analytics, predictive maintenance, etc., to assist in training the AI date models being deployed.
  • The region's ML marketplace is changing due to cloud, and serverless computing makes it possible for developers to get ML applications up and running quickly. Also, the prime driver of the ML as a Service business is information services. The most significant change that serverless computing has brought is that it has eliminated the need to scale physical database hardware.

Competitive Landscape

The machine learning as a service market is highly competitive and consists of several major players. In terms of market share, few major players, currently dominate the market. However, with the advancement of Artificial Intelligence, many companies are increasing their market presence by securing new contracts, by tapping new markets.

  • April 2021 - Microsoft Corporation announced an open Dataset for transportation, health and genomics, labour and economics, population and safety, supplemental and common datasets to improve accuracy of machine learning models with publicly available datasets. This also allows the company to deliver insights at hyperscale using Azure Open Datasets with Azure's machine learning and data analytics solutions that boosts sales of the company's MLaaS

Additional Benefits:

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



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




  • 4.1 Market Overview
  • 4.2 Market Drivers
    • 4.2.1 Increasing Adoption of IoT and Automation
    • 4.2.2 Increasing Adoption of Cloud-based Services
  • 4.3 Market Restraints
    • 4.3.1 Privacy and Data Security Concerns
    • 4.3.2 Need for Skilled Professionals
  • 4.4 Industry Attractiveness - Porter's Five Forces Analysis
    • 4.4.1 Threat of New Entrants
    • 4.4.2 Bargaining Power of Buyers/Consumers
    • 4.4.3 Bargaining Power of Suppliers
    • 4.4.4 Threat of Substitute Products
    • 4.4.5 Intensity of Competitive Rivalry
  • 4.5 Industry Value Chain Analysis
  • 4.6 Assessment of Impact of COVID-19 on the Market


  • 5.1 Application
    • 5.1.1 Marketing and Advertisement
    • 5.1.2 Predictive Maintenance
    • 5.1.3 Automated Network Management
    • 5.1.4 Fraud Detection and Risk Analytics
    • 5.1.5 Other Applications
  • 5.2 Organization Size
    • 5.2.1 Small and Medium Enterprises
    • 5.2.2 Large Enterprises
  • 5.3 End User
    • 5.3.1 IT and Telecom
    • 5.3.2 Automotive
    • 5.3.3 Healthcare
    • 5.3.4 Aerospace and Defense
    • 5.3.5 Retail
    • 5.3.6 Government
    • 5.3.7 BFSI
    • 5.3.8 Other End Users
  • 5.4 Geography
    • 5.4.1 North America
    • 5.4.2 Europe
    • 5.4.3 Asia Pacific
    • 5.4.4 Rest of the World


  • 6.1 Company Profiles*
    • 6.1.1 Microsoft Corporation
    • 6.1.2 IBM Corporation
    • 6.1.3 Google LLC
    • 6.1.4 SAS Institute Inc.
    • 6.1.5 Fair Isaac Corporation (FICO)
    • 6.1.6 Hewlett Packard Enterprise Company
    • 6.1.7 Yottamine Analytics LLC
    • 6.1.8 Amazon Web Services Inc.
    • 6.1.9 BigML Inc.
    • 6.1.10 Iflowsoft Solutions Inc.
    • 6.1.11 Monkeylearn Inc.
    • 6.1.12 Sift Science Inc.
    • 6.1.13 H2O.ai Inc.