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The Machine Learning as a Service (MLaaS) market is encountering dynamic and transformative patterns, shaping the landscape of the innovation business. As organizations increasingly perceive the value of machine learning, MLaaS has arisen as a pivotal player, offering accessible and scalable arrangements. One significant pattern is the rising adoption of MLaaS across different areas, ranging from healthcare and finance to manufacturing and retail. This widespread integration is filled by the longing of organizations to leverage machine learning capabilities without the requirement for broad in-house ability or infrastructure.
Besides, the market is seeing a surge in demand for cloud-based MLaaS arrangements. Cloud platforms give an adaptable and practical climate for conveying machine learning models, allowing organizations to seamlessly scale their operations. The accessibility and scalability of cloud-based MLaaS work on implementation as well as engage organizations to harness the force of machine learning without the weight of significant straightforward ventures. This shift towards cloud-based arrangements aligns with the broader pattern of digital transformation, as organizations look for agile and effective ways to convey advanced innovations.
Another notable pattern shaping the MLaaS market is the increasing emphasis on clarity and interpretability of machine learning models. As machine learning applications become more pervasive in dynamic cycles, the requirement for understanding and confidence in these models has developed. Organizations are focused on arrangements that provide transparency, allowing them to understand the decisions taken by machine learning programs. This trend is especially important in industries with strict regulatory requirements, such as banking and medical care, where clearness is critical for compliance and responsibility.
As machine learning applications become more industry-explicit, merchants are creating redid MLaaS contributions tailored to the interesting necessities and challenges of various areas. This industry-driven approach enhances the relevance and viability of machine learning arrangements, encouraging a more widespread and impactful adoption across different domains.
Machine Learning as a Service Market Highlights:
Machine Learning as a Service (MLaaS) Market Overview
The Machine Learning as a Service (MLaaS) Market is projected to grow from USD 35.05 billion in 2024 to USD 304.82 billion by 2032, exhibiting a compound annual growth rate (CAGR) of 31.04% during the forecast period (2024 - 2032). Additionally, the market size for MLaaS was valued at USD 25.74 billion in 2023.
The market for machine learning market drivers are the increased usage of cloud-based applications, the increased acceptance of automation systems and IoT across most sectors, and the rising need for understanding consumer behavior.
Figure 1: Machine Learning as a Service (MLaaS) Market Size, 2024 - 2032 (USD Billion)
Source: Secondary Research, Primary Research, MRFR Database and Analyst Review
Latest Industry News of Machine Learning as a Service (MLaaS) Market
World-class service is provided by Airbnb thanks to big data and machine learning. Serving more than 80 million visitors worldwide and delivering first-rate service is made possible in large part by data science. Big data and machine learning produce useful information that enables Airbnb to maintain its core values while providing personalised service to each of its customers. An review of the historical data provides specific, suggestive advice on how to improve the level of service and close the gap between what can be profitable and advantageous for both the organisation and the visitors.
Machine Learning as a Service (MLaaS) Market Trends
Increased use of IoT is driving the market growth
Market CAGR for Machine Learning as a Service (MLaaS) supplements is being driven by the growing use of IoT. The use of IoT and automation will rise, propelling the market. IoT operations ensure that the hundreds or more devices connected to a business network are running safely and correctly and that the data being gathered is accurate and timely. Complex back-end analytics engines undertake the heavy lifting of processing the data stream, but outdated methods are routinely used to check the data's integrity. Several providers of IoT platform technologies are enhancing their operations management expertise using machine learning technologies to take control of sizable IoT systems.
As companies implement IoT-based technologies and solutions faster, more firms use machine learning technology for data analytics. Hence, MLaaS would promote IoT innovation. According to Ericsson, the total number of IoT connections is expected to increase from 12.7 billion in 2021 to 32.5 billion in 2030, with a CAGR of 14%. Although MLaaS is already connected to several sensors, it is poised to play a significant role in automation and the Internet of Things.
85% of respondents in a 2019 study by AIOps titled "Status of Automation, Artificial Intelligence, and Machine Learning in Network Management" stated that their business employed many forms of automation. Yet, just 27% of respondents indicated that their business was adequately ready for total automation. Yet, over 65% of research participants said that machine learning was crucial for network management and would probably result in increased automation in the future.Thus, driving the Machine Learning as a Service (MLaaS) market revenue.
Machine Learning as a Service (MLaaS) Market Segment Insights
Machine Learning as a Service (MLaaS) Component Insights
The Machine Learning as a Service (MLaaS) market segmentation, based on component includes Software tools, Cloud APIs, Web-based APIs. The cloud APIs segment dominated the market, accounting for 35% of market revenue. This is due to factors including the growth of end-use industries and application domains in developing nations, which are expected to drive the market for machine learning services. Industry participants are focusing on using cutting-edge technical solutions to improve the utilisation of machine learning services.
Machine Learning as a Service (MLaaS) Organization Size Insights
Based on organization size, the Machine Learning as a Service (MLaaS) market segmentation includes large and small & medium enterprises. The small & medium enterprise category generated the most income (66%). Use of IoT by small businesses might result in significant time savings for the time-consuming machine learning process. In order to extract more meaningful information from the massive data caches created by various devices in the IoT network, MLaaS vendors may perform more queries more quickly and offer more types of analysis.
Figure 1: Machine Learning as a Service (MLaaS) Market, by Distribution channel, 2022 & 2032 (USD billion)
Source: Secondary Research, Primary Research, MRFR Database and Analyst Review
Machine Learning as a Service (MLaaS) Application Insights
Based on Application, the Machine Learning as a Service (MLaaS) market segmentation includes network analytics, predictive maintenance, augmented reality, marketing and advertising, risk analytics, and fraud detection. The marketing and advertising category generated the most income. A recommendation system aims to show customers products they are currently interested in. The following is the marketing work algorithm: Professional marketers develop, evaluate, test, and analyse hypotheses. As information changes every second, this endeavour is time- and labour-intensive, and the outcomes are occasionally unreliable. Marketers may use machine learning to make rapid decisions based on such data.
Machine Learning as a Service (MLaaS) End User Insights
Based on end users, the Machine Learning as a Service (MLaaS) market segmentation includes manufacturing, healthcare, BFSI, transportation, government, and retail. The retail segment held the majority share in 2022, contributing around ~38% concerning the Machine Learning as a Service (MLaaS) market revenue. E-commerce has made a name for itself in the retail trade industry. The retail sector is dynamic and calls for more client connections and adaptability. Retailers use machine learning services to provide customers with fantastic shopping experiences. Large retailers typically use analytical consulting organizations to get the information necessary for marketing. Smaller shops are now able to utilize data to better understand their customer's thanks to the accessibility of cost-effective cloud-based machine learning services, which is anticipated to create opportunity for the expansion of the machine learning as a service sector internationally.
Machine Learning as a Service (MLaaS) Regional Insights
The report breaks down the markets by region, including North America, Europe, Asia-Pacific, and the rest of the world. The North American Machine Learning as a Service (MLaaS) market area will dominate this market; It has a robust infrastructure and the resources to pay for a machine learning as a service solution. Furthermore, the market is predicted to expand during the forecast period due to rising defense spending and technological advancements in the telecommunications industry.
Furthermore, the major countries studied in the market report are Canada, the U.S., German, France, the UK, Italy, Spain, South Korea, China, Japan, India, Australia, and Brazil.
Figure 2: MACHINE LEARNING AS A SERVICE (MLAAS) MARKET SHARE BY REGION 2022 (%)
Source: Secondary Research, Primary Research, MRFR Database and Analyst Review
The second-largest market share belongs to the Europe Machine Learning as a Service (MLaaS) market due to government regulations on data security, which are projected to significantly impact the market for machine learning services. It is projected that services like cloud apps and security information will dominate the industry. Further, the German Machine Learning as a Service (MLaaS) market held the largest market share. The European region's Machine Learning as a Service market grew at the quickest rate in the UK.
The Asia-Pacific Machine Learning as a Service Market is anticipated to see the quickest CAGR between 2023 and 2032. This is because the top firms are focusing on the Asia-Pacific region to expand their operations since this region is expected to see a considerable increase in the deployment of security services in the BFSI industry. Moreover, China’s Machine Learning as a Service (MLaaS) market held the largest market share. The Asia-Pacific region's India Machine Learning as a Service (MLaaS) market has the quickest rate of expansion.
Machine Learning as a Service (MLaaS) Key Market Players & Competitive Insights
The machine learning service (MLaaS) industry will increase further due to major industry participants spending a lot of money on research and development to expand their product portfolio. Significant market developments include new product launches, mutual arrangements, mergers and acquisitions, higher investments, and collaboration with other companies. Market participants also engage in several strategic actions to broaden their worldwide reach. The Machine Learning as a Service (MLaaS) industry must provide cheap products to grow and thrive in an increasingly fiercely competitive climate.
Among the primary business strategy implemented by manufacturers in the worldwide Machine Learning as a Service (MLaaS) industry to assist consumers and grow the market sector is localized manufacturing to cut operating expenses. In recent years, the Machine Learning as a Service (MLaaS) industry has offered some of the most significant advantages. Major players in the Machine Learning as a Service (MLaaS) market, including Microsoft Corporation, Kyndryl, Cognizant, and others, are attempting to increase market demand by investing in research and development operations.
The corporate headquarters of the American technology company Microsoft Corporation are in Redmond, Washington. The Windows family of operating systems, the Microsoft Office package, and the Internet Explorer and Edge web browsers are among Microsoft's most well-known software offerings. The Xbox video gaming consoles and the Microsoft Surface range of touchscreen personal PCs are its two main hardware offerings. In April 2021, To increase the accuracy of machine learning models using publicly available information, Microsoft Corporation launched an open dataset for transportation, health & genomics, labor & economics, population & safety, supplementary, and common datasets. This also enables businesses to use Azure Open Datasets with its machine learning and data analytics solutions to offer hyper-scale insights, increasing sales of these businesses' ML as a Service.
The American analytics software company SAS Institute, or SAS (pronounced "sass"), is headquartered in Cary, North Carolina. SAS creates and sells a collection of analytics software, often known as SAS, that facilitates access to, management of, analysis of, and reporting on data to support decision-making. In June 2019, The SAS Viya platform, its flagship product, now supports users of open-source software. SAS Viya is used for open-source utility and integration. The software user built an API-first strategy that supported a machine learning-powered data preparation procedure.
Key Companies in the Machine Learning as a Service (MLaaS) market include
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Google
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Amazon Web Services
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BigML
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AT&T
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ai
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Yottamine Analytics
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Ersatz Labs, Inc.
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Sift Science, Inc
Machine Learning as a Service (MLaaS) Industry Developments
December 2023:
Bitdeer Technologies Group, an industry pioneer in high-performance computing and blockchain, recently declared a strategic alliance with NVIDIA Corporation, signifying a momentous advancement in its trajectory. This partnership inaugurates Bitdeer AI Cloud, establishing a paradigm shift in the realm of cloud computing and Bitdeer's artificial intelligence capabilities. Bitdeer has emerged as a prominent player in the Bitcoin mining sector since its inception in 2018 under the leadership of Jihan Wu. Presently, the company is expanding its GPU cloud division at an accelerated pace.
NVIDIA, a company widely recognized for its progress in artificial intelligence and graphics, contributes its hardware and software capabilities to the collaboration by appointing Bitdeer as a preferred member of the NVIDIA Partner Network. This partnership signifies the integration of Bitdeer's proficiency in cloud computing with NVIDIA's mastery of AI and machine learning, thereby establishing a foundation for revolutionary advancements in cloud services. By utilizing NVIDIA DGX SuperPOD in conjunction with DGX H100 systems, the Bitdeer AI Cloud is strategically positioned to meet the growing need for AI supercomputing. Leveraging the swiftly expanding public cloud platform-as-a-service market—which grew by more than 32% annually in 2022—this service endeavors to facilitate progress in generative AI, large language models, and other AI workloads. This expansion is primarily attributable to the accelerated advancements in machine learning, AI, and LLM.
Machine Learning as a Service (MLaaS) Market Segmentation
Machine Learning as a Service (MLaaS) Component Outlook
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Software tools
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Cloud APIs
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Web-based APIs
Machine Learning as a Service (MLaaS) Application Outlook
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Network Analytics
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Predictive Maintenance
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Augmented Reality
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Marketing, And Advertising
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Risk Analytics
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Fraud Detection
Machine Learning as a Service (MLaaS) Organization Size Outlook
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Large Enterprise
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Small & Medium Enterprise
Machine Learning as a Service (MLaaS) End-User Outlook
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Manufacturing
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Healthcare
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BFSI
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Transportation
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Government
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Retail
Machine Learning as a Service (MLaaS) Regional Outlook
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North America
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US
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Canada
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Europe
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Germany
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France
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UK
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Italy
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Spain
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Rest of Europe
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Asia-Pacific
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China
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Japan
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India
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Australia
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South Korea
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Australia
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Rest of Asia-Pacific
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Rest of the World
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Middle East
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Africa
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Latin America
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