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市場調査レポート
商品コード
1451433
MLaaS(Machine Learning as a Service)市場レポート:コンポーネント、組織規模、用途、エンドユーザー、地域別、2024-2032Machine Learning as a Service Market Report by Component, Organization Size, Application, End User, and Region 2024-2032 |
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MLaaS(Machine Learning as a Service)市場レポート:コンポーネント、組織規模、用途、エンドユーザー、地域別、2024-2032 |
出版日: 2024年03月02日
発行: IMARC
ページ情報: 英文 148 Pages
納期: 2~3営業日
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世界のMLaaS(Machine Learning as a Service)市場規模は、2023年に75億米ドルに達しました。今後、IMARC Groupは、2024年から2032年にかけて27.24%の成長率(CAGR)を示し、2032年までに697億米ドルに達すると予測しています。組織における人工知能(AI)ソリューションへの需要の高まり、企業におけるクラウドコンピューティングの人気の高まり、世界のビジネスイニシアチブを加速するための自動化重視の高まりが、市場を推進する主な要因のいくつかです。
MLaaS(Machine Learning as a Service)は、クラウドベースのプラットフォームを通じて機械学習機能とインフラへのアクセスを提供する包括的なソリューションです。MLaaSは、ハードウェア、ソフトウェア、専門知識への多額の投資を必要とせずに、企業が機械学習の力を活用することを可能にします。MLaaSは、機械学習モデルの開発、デプロイ、管理を容易にするさまざまなサービス、ツール、リソースを提供します。MLaaSは、開発者やデータサイエンティストが簡単にアクセスし、利用できるように、事前に構築されたアルゴリズムやモデルを幅広く提供します。
世界のMLaaS(Machine Learning As A Service)市場
現在、大規模な社内インフラや専門知識を必要とせずに機械学習(ML)機能を利用できるMLaaSに対する需要の高まりが、市場の成長を後押ししています。これに加えて、効率性と生産性を高め、手作業によるエラーの発生を減らすために、さまざまなビジネス業務の自動化が進んでいることも、市場の成長を後押ししています。さらに、ディープラーニングや強化学習を含むMLアルゴリズムの進歩が進んでいることも、市場の見通しを良好なものにしています。これとは別に、データから貴重な洞察を引き出すために最先端の技術を活用する企業によるMLaaSの採用が増加していることも、市場の成長を支えています。さらに、ビジネスイニシアチブを加速させ、市場投入までの時間を短縮し、迅速な投資収益率(ROI)を実現するために自動化を重視する動きが高まっていることも、市場の成長に寄与しています。
人工知能(AI)ソリューションに対する需要の高まり
現在、さまざまな業界でAIソリューションの採用が増加していることが、MLaaSの需要を促進しています。企業がプロセスの最適化、顧客体験の向上、データからの実用的な洞察の獲得におけるAIの価値を認識するにつれて、MLaaSソリューションの需要が増加しています。企業はMLaaSを活用することで、ハードウェアや専門人材に多額の投資をすることなく、機械学習アルゴリズムのパワーを活用しています。また、MLaaSソリューションは、企業が簡単に実装できる、構築済みの機械学習モデルやデータ処理ツールも提供しています。これにより、中小企業でもAIを利用できるようになり、自社でAIを開発するためのリソースを多く持つ大企業と競争できるようになった。
クラウド・コンピューティングの人気の高まり
クラウド・コンピューティングの人気の高まりは、MLaaSの需要を大きく促進しています。クラウド・コンピューティングは、機械学習モデルを展開するための堅牢でスケーラブルな環境を提供するため、企業は高価なハードウェアやソフトウェアに投資することなく、最先端のML機能を利用することができます。これに加えて、クラウド・コンピューティングは、機械学習に不可欠な大量のデータの保存、処理、分析を容易にします。クラウドベースのMLaaSソリューションは、こうした膨大なデータセットを効率的に処理し、高速データ処理能力とリアルタイム分析を提供することで、迅速な意思決定を可能にし、企業の競争力を高める。さらに、クラウドプラットフォームは、異なる部門、あるいは異なる組織間で、機械学習モデルとデータの容易なコラボレーションとシームレスな共有を保証します。このようなコラボレーションの容易さは、企業がAI主導のデジタルトランスフォーメーションを推進する上で有益であり、MLaaSの導入拡大につながります。
データ世代の増加
現在、世界中でデータ生成量が増加しており、MLaaSの需要を大きく押し上げています。企業がより多くのデータを生成・収集するにつれて、そこから価値を引き出すMLの可能性も高まっています。MLaaSプロバイダーは、貴重な洞察を得て、情報に基づいたビジネス上の意思決定を行うために、これらのデータで訓練することができる既製の機械学習モデルを提供します。さらに、膨大なデータセットのリアルタイム分析は、ペースの速いデータ駆動型のシナリオにおいて極めて重要です。企業は、利用可能な最新の情報に基づいて迅速に意思決定を行う必要があります。大規模なデータセットをリアルタイムで処理する機能を備えたMLaaSプラットフォームは、ビジネスに即時の洞察を提供し、それによって業務効率を向上させ、迅速かつデータ主導の意思決定を可能にします。
The global machine learning as a service (MLaaS) market size reached US$ 7.5 Billion in 2023. Looking forward, IMARC Group expects the market to reach US$ 69.7 Billion by 2032, exhibiting a growth rate (CAGR) of 27.24%during 2024-2032. The growing demand for artificial intelligence (AI) solutions among organizations, rising popularity of cloud computing among businesses, and increasing emphasis on automation to accelerate business initiatives worldwide are some of the major factors propelling the market.
Machine learning as a service (MLaaS) is a comprehensive solution that provides access to machine learning capabilities and infrastructure through a cloud-based platform. It enables organizations to leverage the power of machine learning without the need for significant investments in hardware, software, and specialized expertise. MLaaS offers a range of services, tools, and resources that facilitate the development, deployment, and management of machine learning models. It provides a wide array of pre-built algorithms and models that can be easily accessed and utilized by developers and data scientists.
Global Machine Learning As A Service (MLaaS) Market
At present, the increasing demand for MLaaS to access machine learning (ML) capabilities without the need for extensive in-house infrastructure and expertise is impelling the growth of the market. Besides this, the rising automation of various business operations to increase efficiency and productivity and reduce the occurrence of manual errors is propelling the growth of the market. In addition, the growing advancements in ML algorithms, including deep learning and reinforcement learning, are offering a favorable market outlook. Apart from this, the increasing employment of MLaaS by businesses to leverage cutting-edge techniques to extract valuable insights from their data is supporting the growth of the market. Additionally, the rising emphasis on automation to accelerate business initiatives, achieve faster time-to-time markets, and realize quicker returns on investments (ROI) is contributing to the growth of the market.
Rising demand for artificial intelligence (AI) solutions
At present, the increasing employment of AI solutions across various industries is fueling the demand for MLaaS. As organizations recognize the value of AI in optimizing processes, enhancing customer experiences, and gaining actionable insights from data, the demand for MLaaS solutions is increasing. Businesses are leveraging MLaaS to harness the power of machine learning algorithms without the need for significant investments in hardware and specialized talent. MLaaS solutions also offer pre-built machine learning models and data handling tools which businesses can easily implement. It has made AI accessible to small and medium-sized businesses, enabling them to compete with larger companies that have more resources for developing AI in-house.
Growing popularity of cloud computing
The rising popularity of cloud computing is significantly driving the demand for MLaaS as it provides a robust and scalable environment for deploying machine learning models, enabling businesses to access cutting-edge ML capabilities without investing in expensive hardware or software. Besides this, cloud computing facilitates easy storage, processing, and analysis of large volumes of data, which are crucial for machine learning. Cloud-based MLaaS solutions can handle these vast datasets efficiently, providing high-speed data processing capabilities and real-time analytics, thereby enabling quick decision-making and creating a competitive edge for businesses. In addition, cloud platforms ensure easy collaboration and seamless sharing of machine learning models and data across different departments or even different organizations. This ease of collaboration can be instrumental in businesses to drive AI-driven digital transformation, thereby leading to increased uptake of MLaaS.
Increasing generation of data
Presently, there is an increase in data generation worldwide, which is significantly propelling the demand for MLaaS. As businesses generate and collect more data, the potential for ML to extract value from it also increases. MLaaS providers deliver ready-made machine learning models that can be trained on this data to gain valuable insights and make informed business decisions. Moreover, the real-time analysis of massive datasets is crucial in fast-paced, data-driven scenarios. Businesses need to make decisions quickly based on the latest information available. MLaaS platforms, equipped with the capability to process large datasets in real time, can provide businesses with immediate insights, thereby improving their operational efficiency and enabling swift and data-driven decision-making.
IMARC Group provides an analysis of the key trends in each segment of the global machine learning as a service (MLaaS) market report, along with forecasts at the global and regional levels from 2024-2032. Our report has categorized the market based on component, organization size, application and end user.
Software
Services
Services dominate the market
The report has provided a detailed breakup and analysis of the market based on the component. This includes software and services. According to the report, services represented the largest segment.
MLaaS providers offer pre-built and customizable machine learning models, which simplifies the adoption of machine learning technologies, especially for small and medium enterprises (SMEs) that may lack the resources or expertise to develop these models in-house. Developing and implementing machine learning models in-house can be quite expensive, considering the costs of hiring skilled data scientists, investing in robust hardware, and maintaining the necessary software. MLaaS provides a more cost-effective alternative as it operates on a pay-as-you-go model, allowing businesses to only pay for what they use. MLaaS providers also offer ongoing support and maintenance services, which can help businesses overcome any challenges they encounter when using the technology. This support can help businesses mitigate risks and ensure that their machine-learning models are performing optimally.
Small and Medium-sized Enterprises
Large Enterprises
Large enterprises hold the largest share in the market
A detailed breakup and analysis of the market based on the organization size has also been provided in the report. This includes small and medium-sized enterprises and large enterprises. According to the report, large enterprises accounted for the largest market share.
Large enterprises are increasingly turning to machine learning as a service (MLaaS) as it is a convenient, scalable, and cost-effective solution for implementing advanced machine learning capabilities, allowing large businesses to make data-driven decisions and gain a competitive edge. The vast amount of data generated by these enterprises necessitates efficient tools to extract meaningful insights, and MLaaS offers robust machine-learning models capable of processing this information swiftly and effectively. Moreover, in a dynamic business environment, large enterprises need to respond spontaneously to changing market conditions. With MLaaS, they can leverage real-time analytics to derive immediate insights from their data, enhancing their decision-making process and operational efficiency. This is particularly beneficial for industries that operate in fast-paced environments, such as finance, technology, and e-commerce.
Marketing and Advertising
Fraud Detection and Risk Management
Predictive Analytics
Augmented and Virtual Reality
Natural Language Processing
Computer Vision
Security and Surveillance
Others
Marketing and advertising hold the biggest share in the market
A detailed breakup and analysis of the market based on the application have also been provided in the report. This includes marketing and advertising, fraud detection and risk management, predictive analytics, augmented and virtual reality, natural language processing, computer vision, security and surveillance, and others. According to the report, marketing and advertising accounted for the largest market share.
Marketing and advertising industries increasingly require machine learning as a service (MLaaS) due to its potential to transform their operations and customer engagements significantly. In these fields, understanding consumer behavior and preferences is of utmost importance, and the ability to analyze vast amounts of customer data is vital. MLaaS provides robust machine learning models that can process and analyze this data, offering valuable insights about customers, enabling personalized marketing, and improving target advertising. MLaaS is also used to segment customers based on various characteristics, enabling marketers to tailor their messages and offers to specific groups. It allows for precise targeting, which can significantly enhance the effectiveness of marketing campaigns.
IT and Telecom
Automotive
Healthcare
Aerospace and Defense
Retail
Government
BFSI
Others
BFSI holds the maximum share of the market
A detailed breakup and analysis of the market based on the end user have also been provided in the report. This includes IT and telecom, automotive, healthcare, aerospace and defense, retail, government, BFSI, and others. According to the report, BFSI accounted for the largest market share.
The banking, financial services and insurance (BFSI) sector is relying on machine learning as a service (MLaaS) due to its transformative potential to streamline operations, enhance customer experiences, and bolster security measures. The BFSI sector deals with enormous amounts of data, and MLaaS provides an efficient way to process, analyze, and draw actionable insights from this data, enabling financial institutions to make informed decisions. MLaaS plays a pivotal role in personalizing customer experiences in the BFSI sector. By analyzing customer data, machine learning models can identify individual behaviors and preferences, enabling financial institutions to tailor their services to each customer's unique needs. Furthermore, by leveraging MLaaS, financial institutions can build predictive models that can alert them to potential fraud or risks in real-time, significantly enhancing their security measures and customer trust.
North America
United States
Canada
Asia-Pacific
China
Japan
India
South Korea
Australia
Indonesia
Others
Europe
Germany
France
United Kingdom
Italy
Spain
Russia
Others
Latin America
Brazil
Mexico
Others
Middle East and Africa
North America exhibits a clear dominance, accounting for the largest machine learning as a service (MLaaS) market share
The report has also provided a comprehensive analysis of all the major regional markets, which include North America (the United States and Canada); Asia Pacific (China, Japan, India, South Korea, Australia, Indonesia, and Others); Europe (Germany, France, the United Kingdom, Italy, Spain, Russia, and others); Latin America (Brazil, Mexico, and others); and the Middle East and Africa.
North America held the biggest market share due to the rising number of businesses that are integrating AI and ML in their operations to achieve efficiency and scalability and minimize the involvement of humans.
Another contributing aspect is the rising generation of data through various online channels. Besides this, the increasing number of cyber threats and data breaches is propelling the growth of the market.
Asia Pacific is estimated to expand further in this domain due to the rising popularity of cloud computing and edge computing. Apart from this, the rising focus on automating various business operations is strengthening the growth of the market.
Key market players are investing in research operations to improve their machine-learning services. They are also providing cutting-edge machine learning tools and capabilities that are efficient, scalable, and easy to use. Top companies are entering into strategic partnerships with other tech companies, startups, and research institutions to deliver more comprehensive and innovative solutions. They are also focusing on providing training and certification programs to create a skilled workforce. Leading companies are taking initiatives to enhance the security features of their platforms. They are implementing stronger data encryption, enhancing access controls, and using machine learning to detect and respond to security threats.
Amazon.com Inc.
Bigml Inc.
Fair Isaac Corporation
Google LLC (Alphabet Inc.)
H2O.ai Inc.
Hewlett Packard Enterprise Development LP
Iflowsoft Solutions Inc.
International Business Machines Corporation
Microsoft Corporation
MonkeyLearn
Sas Institute Inc.
Yottamine Analytics Inc.
In March 2023, Amazon Web Services, and Amazon.com Inc. company, announced a collaboration with NVIDIA to build the world's most scalable, on-demand artificial intelligence (AI) infrastructure optimized to train large machine learning models and build generative AI applications.
In September 2018, Fair Isaac Corporation announced the launch of the latest version of FICO(R) Analytics Workbench(TM), which assists data scientists to understand the machine learning models behind AI-derived decisions.
In July 2023, International Business Machines Corporation announced the launch of Watsonx, which comprise three products to help businesses accelerate and scale AI and machine learning.