Machine Learning as a Service (MLaaS) Market – Growth, Trends, COVID-19 Impact and Forecast (2022)


The Machine Learning-as-a-Service (MLaaS) Market (hereafter referred to as the Market Researched) was valued at USD 2.26 Billion in 2021, and it is projected to reach USD 16.7 Billion by 2027, registering a CAGR of 39.

New York, June 13, 2022 (GLOBE NEWSWIRE) — announces the publication of the report “Machine Learning as a Service (MLaaS) Market – Growth, Trends, COVID-19 Impact, and Forecasts (2022 – 2027)” – https ://
25% during the period 2022-2027 (hereinafter referred to as the forecast period).

Machine Learning (ML) is a subfield of Artificial Intelligence (AI) that enables training algorithms to perform classifications or predictions through the use of statistical methods, revealing key insights in projects data mining. This information then guides decision-making within applications and businesses, ideally impacting key growth metrics. Since it is about algorithms, model complexity and computational complexity, it requires skilled professionals to develop these solutions.
With advances in data science and artificial intelligence, the performance of machine learning has accelerated at a rapid pace. Companies are identifying the potential of this technology and hence the adoption rate of it is expected to increase over the forecast period. Companies offer machine learning solutions on a subscription model, making it easier for consumers to take advantage of this technology. Plus, it offers flexibility on a pay-as-you-go basis.
Machine Learning as a Service (MLaaS) is a suite of services that provides machine learning tools as part of cloud computing services. These vendor services offer tools that include data visualization, APIs, facial 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 being able to choose from a wide variety of solutions focused on different business needs. Furthermore, factors such as increasing adoption of cloud-based services, IoT and automation, as well as growing demand for consumer behavior analytics, are expected to drive the growth of the machine learning market as a service.
Machine learning as a service leverages deep learning techniques for predictive analytics to improve decision making. However, using MLaaS presents security challenges for ML model owners and data privacy challenges for data owners. Data owners are concerned about the privacy and security of their data on MLaaS platforms. In contrast, owners of MLaaS platforms fear that their models will be stolen by adversaries posing as customers.
The COVID-19 pandemic has prompted many organizations to accelerate their migrations to public cloud solutions because the elasticity of cloud services can respond to unexpected spikes in service demand. Cloud migrations have helped companies reinvent the way they do business in the time of COVID-19. The need for AI services has increased and many cloud providers are offering AIaaS and MLaaS.

Main market trends

Increase adoption of IoT and automation to drive the market

IoT operations ensure that the thousands or more devices operate properly and securely on a corporate network and that the data collected is both accurate and timely. While sophisticated back-end analytics engines do the heavy lifting of processing the data stream, ensuring data quality is often left to outdated methodologies. To ensure control over sprawling IoT infrastructures, some IoT platform vendors are using machine learning technology to bolster their operations management capabilities.
Machine learning can demystify hidden patterns in IoT data by analyzing large volumes of data using sophisticated algorithms. ML inference can complement or replace manual processes with automated systems using actions derived from statistics in critical processes. ML-based solutions automate the process of modeling IoT data, eliminating the time-consuming and labor-intensive activities of model selection, coding, and validation.
Small businesses that embrace the IoT can significantly save on the time-consuming machine learning process. MLaaS providers can perform more queries faster, providing more types of analysis to gain more actionable insights from large caches of data generated by multiple devices in the IoT network.
As companies increasingly adopt IoT-based technologies and solutions, more companies are leveraging machine learning technologies for data analysis. As a result, MLaaS is expected to drive innovation in IoT. According to Ericsson, the total number of IoT connections is expected to increase from 12.4 billion in 2020 to 26.4 billion in 2026, with a CAGR of 13%. Although MLaaS already integrates with various sensors, MLaaS is poised to play a vital 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 said their organization had more than one type of automation. However, only 27% of respondents felt their organization was well prepared for full automation. However, around 65% of respondents said that machine learning is very critical to network management, and should drive future automation.

North America is expected to hold the largest market share

North America is expected to hold a significant share of the market owing to the strong innovation ecosystem, fueled by strategic federal investments in cutting-edge technologies, complemented by the presence of visionary scientists and entrepreneurs from research institutions of world renown, which propelled the development of MLaaS.
The region is also experiencing significant proliferation of 5G, IoT and connected devices. As a result, communications service providers (CSPs) must effectively manage ever-increasing complexity through virtualization, network slicing, new use cases, and service requirements. This should boost MLaaS solutions, as traditional approaches to managing networks and services are no longer sustainable.
Additionally, major tech companies in the region, such as Microsoft, Google, Amazon, and IBM, have become major players in the race for ML as a service. Since each of the companies has significant public cloud infrastructure and ML platforms, this allows companies to make machine learning as a service a reality for those looking to use AI for everything. , from customer service and robotic process automation to marketing and analytics. , predictive maintenance, etc., to help train AI date models being deployed.
The region’s ML market is evolving due to the cloud, and serverless computing allows developers to quickly build ML applications. Moreover, the primary driver of the ML-as-a-service business is information services. The most significant change that serverless computing has brought is the elimination of the need to scale physical database hardware.
These trends are enabling vendors to introduce ML as a service to simplify the adoption of ML in enterprise and SMB adoption. For example, in December 2020, Calligo launched MLaas to expand the company’s portfolio of managed data services to improve the productivity of businesses, such as SMBs and enterprises, by ensuring privacy, quality, data security and accuracy. This helps businesses avoid cost issues by eliminating the need to hire a data science resource.

Competitive landscape

The strong market consolidation has increased the competition among leading players such as Microsoft, IBM, Google and Amazon. To capture a significant share of the market, other players are actively expanding their product portfolios and geographic presence.

February 2022 – Telecom giant AT&T and AI company H2O have collaborated and launched an AI feature store for businesses. This provides a repository for collaborating, sharing, reusing, and discovering machine learning capabilities to accelerate AI project deployments and improve ROI.
December 2021 – AWS announced six new Amazon SageMaker features. This will make machine learning even more accessible and profitable. This brings together powerful new features, including a no-code environment for creating accurate machine learning predictions and more accurate data labeling using highly skilled annotators.
November 2021 – SAS has added support for open source users to its flagship SAS Viya platform. SAS Viya is intended for open source integration and utility. The software user established an API-driven strategy that powered a data preparation process with machine learning.

Additional benefits:

The Market Estimate (ME) sheet in Excel format
3 months of analyst support
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