The Machine Learning as a Service (MLaaS) Market (hereafter referred to as the market studied) was valued at USD 2. 26 billion in 2021, and it is expected to reach USD 16. 7 billion by 2027, registering a CAGR of 39.
New York, June 13, 2022 (GLOBE NEWSWIRE) -- Reportlinker.com announces the release of the report "Machine Learning as a Service (MLaaS) Market - Growth, Trends, COVID-19 Impact, and Forecasts (2022 - 2027)" - https://www.reportlinker.com/p06106023/?utm_source=GNW
25% during the period of 2022-2027 (henceforth, referred to as the forecast period).
Machine learning (ML) is a subfield of artificial intelligence (AI) that enables training algorithms to make classifications or predictions through the use of statistical methods, uncovering key insights within data mining projects. These insights subsequently drive decision-making within applications and businesses, ideally impacting key growth metrics. 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 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.
Machine learning-as-a-service (MLaaS) is a range of services that provides machine-learning tools as part of Cloud Computing Services. These services from providers offer tools that include data visualization, APIs, face recognition, natural language processing, predictive analytics, and deep learning. The actual computation is handled by the provider’s data centers. 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.
Machine learning-as-a-service leverages deep learning techniques for predictive analytics to enhance decision-making. However, the usage of MLaaS introduces security challenges for ML model owners and data privacy challenges for data owners. Data owners are concerned about the privacy and safety of their data on MLaaS platforms. In contrast, MLaaS platform owners worry that their models may be stolen by adversaries who pose as clients.
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.
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 being collected is both timely and accurate. While the sophisticated back-end analytics engines work on the heavy lifting of processing the data stream, 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 may demystify the hidden patterns in IoT data by analyzing significant volumes of data utilizing sophisticated algorithms. ML inference may 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 may significantly save on the time-consuming machine learning process. MLaaS vendors may 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 adopt IoT-based technologies and solutions increasingly, 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 were poised to increase from 12.4 billion in 2020 to 26.4 billion in 2026, with a CAGR of 13%. Although MLaaS already integrate with various sensors, MLaaS is poised to a critical role in IoT and automation.
In the State of Automation, artificial intelligence, and machine learning in network management study published by AIOps, in 2019, 85% of respondents surveyed stated that their organization had more than one type of automation. However, only 27% of respondents felt their organization was well prepared for full automation. However, about 65% of respondents surveyed stated that machine learning was highly critical for network management, and it is expected to drive future automation.
North America is Expected to Hold Largest Market Share
North America is expected to hold a significant share in the market owing to the robust innovation ecosystem, fueled by strategic federal investments into advanced technology, complemented by the presence of visionary scientists and entrepreneurs coming together from globally renowned research institutions, which has propelled the development of MLaaS.
The region is also witnessing a significant proliferation of 5G, IoT, and connected devices. As a result, communications service providers (CSPs) need to manage an ever-growing complexity efficiently 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 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 machine learning-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. Additionally, the prime driver of the ML-as-a-service business is information services. The most significant change that serverless computing has brought is eliminating the need to scale physical database hardware.
Such trends allow vendors to introduce ML-as-a-service to simplify the adoption of ML in enterprise adoption and SME. For instance, in December 2020, Calligo launched MLaas to expand the company’s managed data service portfolio to enhance the productivity of businesses, such as SMEs and enterprises, by ensuring data privacy, data quality, security, and accuracy. This helps businesses avoid cost challenges by eliminating the need to hire a data science resource.
The high market consolidation has increased the competition among prominent players such as Microsoft, IBM, Google, and Amazon. To capture a significant share in the market, other players are actively expanding their product portfolios and geographical presence.
February 2022 - Telecom giant AT&T and AI company H2O have collaborated and launched an artificial intelligence feature store for enterprises. This delivers a repository for collaborating, sharing, reusing, and discovering machine learning features to speed AI project deployments and improve ROI.
December 2021 - AWS announced six new Amazon SageMaker capabilities. This will make machine learning even more accessible and cost-effective. This brings together powerful new capabilities, including a no-code environment for creating accurate machine learning predictions more accurate data labeling using highly skilled annotators.
November 2021 - SAS added support for open-source users to its flagship SAS Viya platform. SAS Viya is for open-source integration and utility. The software user established an API-first strategy that fueled a data preparation process with machine learning.
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