Machine Learning as a Service (MLaaS) Market - Growth, Trends, COVID-19 Impact, and Forecasts (2021 - 2026)

Market Overview

The Machine Learning-as-a-service (MLaaS) Market was valued at USD 1.60 billion in 2020, and it is expected to reach USD 12.10 billion by 2026, registering a CAGR of 39.86% during the period of 2021-2026. 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. As a result, the global cloud market recorded significant growth in the healthcare segment in 2020. AI and ML technology is being used considerably to fight COVID-19.

• Machine learning (ML), a subfield of artificial intelligence (AI) in its most straightforward description, spans a broad set of algorithms that are used to extract valuable models from raw data and grew out of traditional statistics and analysis. 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 now 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. MLaaS products offered by companies are microservices offered by significant cloud computing firms like Amazon Web Services, Microsoft Azure, and Google Cloud Platform. These solutions typically include pre-built natural language processing (NLP), computer vision, and general machine learning algorithms.
• 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.
• As the emergence of electronic sensors, connected machines, and equipment in the industry continues, reinforced by the advancements in network connectivity technology, the demand for MLaaS is expected to grow considerably over the forecast period. Furthermore, industries have emerged into big data generators and need a highly efficient supporting system for gaining insight promptly.

Scope of the Report

Machine learning-as-a-service (MLaaS) is a broad range of services that offer machine learning (ML) tools as a feature of cloud computing services. MLaaS suppliers offer tools including APIs, data visualization, natural language processing, predictive analytics, and face recognition. The supplier’s cloud infrastructure handles all the actual computation. The study provides in-depth perspective of the major segments such application, end user, and geography.

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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 that is being collected is both timely and accurate. While the sophisticated back-end analytics engines work on the heavy lifting of processing the stream of data, 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 could engage in demystifying the hidden patterns in IoT data by analyzing significant volumes of data utilizing sophisticated algorithms. ML inference could 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 could significantly save on the time-consuming process of machine learning. MLaaS vendors could 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 increasingly adopt IoT-based technologies and solutions, 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 are poised to increase from 12.4 billion in 2020 to 26.4 billion in 2026, with a CAGR of 13%. Although MLaaS already has the capacity to integrate with various kinds of sensors, MLaaS is poised to a critical role in IoT and automation.

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