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市场调查报告书
商品编码
1642465
2025-2033 年按组件、组织规模、应用程式、最终用户和区域分類的机器学习即服务市场报告Machine Learning as a Service Market Report by Component, Organization Size, Application, End User, and Region 2025-2033 |
2024年,全球机器学习即IMARC Group(MLaaS)市场规模达96亿美元。对基于云端的解决方案不断增长的需求、人工智慧 (AI) 的进步、物联网 (IoT) 设备资料的激增以及金融、医疗保健和零售等行业对预测分析的需求是推动这一趋势的一些因素。
机器学习即服务 (MLaaS) 是一种综合解决方案,可透过基于云端的平台提供对机器学习功能和基础架构的存取。它使组织能够利用机器学习的力量,而无需在硬体、软体和专业知识方面进行大量投资。 MLaaS 提供一系列服务、工具和资源,促进机器学习模型的开发、部署和管理。它提供了广泛的预先建构演算法和模型,开发人员和资料科学家可以轻鬆存取和使用这些演算法和模型。
全球机器学习即服务 (MLaaS) 市场
目前,对 MLaaS 存取机器学习 (ML) 功能而不需要大量内部基础设施和专业知识的需求不断增长,正在推动市场的成长。除此之外,各种业务营运的自动化程度不断提高,以提高效率和生产力并减少人工错误的发生,正在推动市场的成长。此外,深度学习和强化学习等机器学习演算法的不断进步也带来了良好的市场前景。除此之外,企业越来越多地使用 MLaaS 来利用尖端技术从资料中提取有价值的见解,这正在支持市场的成长。此外,为了加速业务计划、实现更快的市场投放速度以及更快地实现投资回报 (ROI),人们越来越重视自动化,这也促进了市场的成长。
对人工智慧 (AI) 解决方案的需求不断增长
目前,人工智慧解决方案在各行业的应用不断增加,推动了对 MLaaS 的需求。随着组织认识到人工智慧在优化流程、增强客户体验以及从资料中获取可行见解的价值,对 MLaaS 解决方案的需求正在增加。企业正在利用 MLaaS 来利用机器学习演算法的强大功能,而无需在硬体和专业人才方面进行大量投资。 MLaaS 解决方案还提供企业可以轻鬆实施的预先建置机器学习模型和资料处理工具。它使中小型企业能够使用人工智慧,使它们能够与拥有更多内部开发人工智慧资源的大公司竞争。
云端运算日益普及
云端运算的日益普及极大地推动了对 MLaaS 的需求,因为它为部署机器学习模型提供了强大且可扩展的环境,使企业能够存取尖端的 ML 功能,而无需投资昂贵的硬体或软体。除此之外,云端运算有助于轻鬆储存、处理和分析大量资料,这对于机器学习至关重要。基于云端的MLaaS解决方案可以有效地处理这些庞大的资料集,提供高速资料处理能力和即时分析,从而实现快速决策并为企业创造竞争优势。此外,云端平台可确保不同部门甚至不同组织之间机器学习模式和资料的轻鬆协作和无缝共享。这种轻鬆的协作有助于企业推动人工智慧驱动的数位转型,进而提高 MLaaS 的采用率。
增加资料生成
目前,全球资料产生量不断增加,这大大推动了对 MLaaS 的需求。随着企业产生和收集更多资料,机器学习从中提取价值的潜力也随之增加。 MLaaS 提供者提供现成的机器学习模型,可以根据这些资料进行训练,以获得有价值的见解并做出明智的业务决策。此外,海量资料集的即时分析在快节奏、数据驱动的场景中至关重要。企业需要根据可用的最新资讯快速做出决策。 MLaaS平台具备即时处理大型资料集的能力,可为企业提供即时洞察,从而提高营运效率并实现快速的资料驱动决策。
软体
服务
服务主导市场
MLaaS 供应商提供预先建置和可自订的机器学习模型,这简化了机器学习技术的采用,特别是对于可能缺乏资源或专业知识来内部开发这些模型的中小型企业 (SME)。考虑到僱用熟练的资料科学家、投资强大的硬体以及维护必要的软体的成本,内部开发和实施机器学习模型可能非常昂贵。 MLaaS 提供了一种更具成本效益的替代方案,因为它采用即用即付模式运行,允许企业只需为他们使用的内容付费。 MLaaS 供应商还提供持续的支援和维护服务,可以帮助企业克服在使用该技术时遇到的任何挑战。这种支援可以帮助企业降低风险并确保其机器学习模型发挥最佳性能。
中小企业
大型企业
大企业占最大市场份额
大型企业越来越多地转向机器学习即服务(MLaaS),因为它是一种方便、可扩展且经济高效的解决方案,用于实施高级机器学习功能,使大型企业能够做出数据驱动的决策并获得竞争优势。这些企业产生的大量资料需要高效的工具来提取有意义的见解,而 MLaaS 提供了强大的机器学习模型,能够快速有效地处理这些资讯。此外,在动态的商业环境中,大企业需要自发性地应对不断变化的市场状况。借助 MLaaS,他们可以利用即时分析从资料中获取即时见解,从而增强决策流程和营运效率。这对于在快节奏环境中运营的行业(例如金融、技术和电子商务)尤其有利。
行销和广告
诈欺侦测和风险管理
预测分析
扩增实境和虚拟现实
自然语言处理
电脑视觉
安全与监控
其他的
行销和广告占市场最大份额
行销和广告业越来越需要机器学习即服务 (MLaaS),因为它有可能显着改变其营运和客户参与度。在这些领域,了解消费者行为和偏好至关重要,而分析大量客户资料的能力也至关重要。 MLaaS 提供强大的机器学习模型,可以处理和分析这些资料,提供有关客户的宝贵见解,实现个人化行销并改善目标广告。 MLaaS 也用于根据各种特征对客户进行细分,使行销人员能够针对特定群体自订他们的讯息和优惠。它可以实现精确定位,从而显着提高行销活动的有效性。
资讯科技和电信
汽车
卫生保健
航太和国防
零售
政府
BFSI
其他的
BFSI 拥有最大市场份额
银行、金融服务和保险 (BFSI) 产业正在依赖机器学习即服务 (MLaaS),因为它具有简化营运、增强客户体验和加强安全措施的变革潜力。 BFSI 部门处理大量资料,MLaaS 提供了一种有效的方法来处理、分析这些资料并从中得出可行的见解,使金融机构能够做出明智的决策。 MLaaS 在 BFSI 领域的个人化客户体验方面发挥关键作用。透过分析客户资料,机器学习模型可以识别个人行为和偏好,使金融机构能够根据每个客户的独特需求量身定制服务。此外,透过利用 MLaaS,金融机构可以建立预测模型,即时提醒潜在的诈欺或风险,从而显着增强其安全措施和客户信任。
北美洲
美国
加拿大
亚太
中国
日本
印度
韩国
澳洲
印尼
其他的
欧洲
德国
法国
英国
义大利
西班牙
俄罗斯
其他的
拉丁美洲
巴西
墨西哥
其他的
中东和非洲
北美表现出明显的主导地位,占据最大的机器学习即服务 (MLaaS) 市场份额
该报告还对所有主要区域市场进行了全面分析,其中包括北美(美国和加拿大);亚太地区(中国、日本、印度、韩国、澳洲、印尼等);欧洲(德国、法国、英国、义大利、西班牙、俄罗斯等);拉丁美洲(巴西、墨西哥等);以及中东和非洲。
由于越来越多的企业将人工智慧和机器学习整合到其营运中,以实现效率和可扩展性并最大限度地减少人类的参与,北美占据了最大的市场份额。
另一个贡献因素是透过各种线上管道产生的资料不断增加。除此之外,网路威胁和资料外洩的数量不断增加正在推动市场的成长。
由于云端运算和边缘运算的日益普及,亚太地区预计将在这一领域进一步扩张。除此之外,对各种业务营运自动化的日益关注正在加强市场的成长。
主要市场参与者正在投资研究业务,以改善其机器学习服务。他们还提供高效、可扩展且易于使用的尖端机器学习工具和功能。顶尖公司正在与其他科技公司、新创公司和研究机构建立策略合作伙伴关係,以提供更全面和创新的解决方案。他们还专注于提供培训和认证计划,以培养熟练的劳动力。领先的公司正在采取措施增强其平台的安全功能。他们正在实施更强大的资料加密,增强存取控制,并使用机器学习来侦测和回应安全威胁。
亚马逊公司
比格姆公司
费尔艾萨克公司
谷歌有限责任公司(Alphabet Inc.)
H2O.ai公司
惠普企业开发有限公司
Iflowsoft 解决方案公司
国际商业机器公司
微软公司
猴子学习
Sas研究所公司
约塔明分析公司
The global machine learning as a service (MLaaS) market size reached USD 9.6 Billion in 2024. Looking forward, IMARC Group expects the market to reach USD 84.1 Billion by 2033, exhibiting a growth rate (CAGR) of 25.88% during 2025-2033. The growing demand for cloud-based solutions, advancements in artificial intelligence (AI), proliferation of data from internet of things (IoT) devices, and the need for predictive analytics in industries including finance, healthcare, and retail are some of the factors propelling the market growth.
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.
Software
Services
Services dominate the market
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
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
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
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.