 | • レポートコード：MRC2108A324 • 出版社/出版日：Mordor Intelligence / 2021年7月31日 • レポート形態：英文、PDF、150ページ • 納品方法：Eメール（受注後2-3営業日） • 産業分類：情報・通信 |
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レポート概要
| Mordor Intelligence社は、サービスとしての機械学習（MLaaS）の世界市場規模が2020年16.0億ドルから2026年121.0億ドルまで、2021年から2026年の間に年平均39.86％成長すると予測しています。本調査資料では、世界のサービスとしての機械学習（MLaaS）市場について調査し、イントロダクション、調査手法、エグゼクティブサマリー、市場動向、用途別（マーケティング・広告、予知保全、自動ネットワーク管理、不正検出・リスク分析、その他）分析、組織規模別（中小企業、大企業）分析、エンドユーザー別（IT・通信、自動車、医療、航空宇宙・防衛、小売、その他）分析、地域別分析（北米、ヨーロッパ、アジア太平洋、その他）、競争状況、投資分析、市場の将来などを掲載しています。 ・イントロダクション ・調査手法 ・エグゼクティブサマリー ・市場動向 ・サービスとしての機械学習（MLaaS）の世界市場規模：用途別（マーケティング・広告、予知保全、自動ネットワーク管理、不正検出・リスク分析、その他） ・サービスとしての機械学習（MLaaS）の世界市場規模：組織規模別（中小企業、大企業） ・サービスとしての機械学習（MLaaS）の世界市場規模：エンドユーザー別（IT・通信、自動車、医療、航空宇宙・防衛、小売、その他） ・サービスとしての機械学習（MLaaS）の世界市場規模：地域別（北米、ヨーロッパ、アジア太平洋、その他） ・競争状況（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.、Monkeylearn Inc.、Sift Science Inc.、H2O.ai Inc.） ・投資分析 ・市場の将来 |
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.
- 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
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1 INTRODUCTION
1.1 Study Assumptions and Market Definition
1.2 Scope of the Study
2 RESEARCH METHODOLOGY
3 EXECUTIVE SUMMARY
4 MARKET DYNAMICS
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 MARKET SEGMENTATION
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 COMPETITIVE LANDSCAPE
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.
7 INVESTMENT ANALYSIS
8 FUTURE OF THE MARKET