Published Date : 22 Dec 2022
The global machine learning as a service market size was exhibited at USD 21.55 billion in 2022 and is projected to attain around USD 305.62 billion by 2032, growing at a CAGR of 39.3% during the forecast period 2022 to 2030.
Machine learning is a method of data analysis that combines statistical data analysis with non-explicit programming to get desired prediction results. It is made to include cognitive computing and artificial intelligence (AI) features using a number of algorithms, and it is utilised to comprehend the connections between datasets in order to provide the necessary results. Cloud computing services that provide machine learning tools are collectively referred to as "machine learning as a service" (MLaaS).
The rise of the cloud computing industry, artificial intelligence, and cognitive computing are the main factors driving the machine learning as a service market. The rise in acceptance of analytical solutions, the growth of the artificial intelligence and cognitive computing markets, the expansion of application domains, and the shortage of qualified people are some of the key issues affecting the machine learning as a service industry.
Machine Learning as a Service Market Report Scope
|Market Size in 2022||
USD 21.55 Billion
|Market Size by 2030||
USD 305.62 Billion
|Growth Rate from 2022 to 2030||CAGR of 39.3%|
|Forecast Period||2022 to 2030|
|Segments Covered||Component, Organization Size, Application, and Industry Vertical|
|Region Covered||North America, Europe, Asia-Pacific, Latin America and Middle East & Africa|
Google Inc, SaS Institute Inc, Fico, Hewlett Packard Enterprise, Yottamine Analytics, Amazon Web Services, Bigml, Inc, Microsoft Corporation, Predictron Labs Ltd, Lbm Corporation
Because of North America's strong innovation environment, which is supported by smart federal investments in cutting-edge technology and the presence of brilliant scientists and entrepreneurs from top research institutions across the world, MLaaS is likely to grow significantly in the next years. Additionally, 5G, IoT, and linked gadgets are significantly proliferating in the region. As a result, through network slicing, virtualization, novel use cases, and service needs, communications service providers (CSPs) must effectively manage an ever-increasing complexity.
Due to the unsustainable nature of conventional network and service management strategies, MLaaS solutions are anticipated to be driven by this. The cloud is transforming the region's machine learning business, and serverless computing enables developers to swiftly launch ML apps. Information services are also the main force behind the ML-as-a-service industry. The requirement to scale actual database hardware is no longer necessary, which is the most important shift brought about by serverless computing.
The sector is expanding as a result of social media platforms and cloud computing technologies' rising popularity. All organizations that offer enterprise storage solutions today frequently employ cloud computing. Online data analysis utilizing cloud storage has the benefit of analyzing real-time data gathered on the cloud. Data analysis is possible at any time and from any location thanks to cloud computing. Additionally, leveraging the cloud to use machine learning enables organizations to virtually access important data from linked data warehouses, reducing infrastructure and storage expenses, such as customer behavior and purchase trends. As a result of the increased use of cloud computing, machine learning as a service industry is expanding.
Artificial intelligence (AI) systems employ machine learning to support reasoning, learning, and self-correction. Examples of AI applications include expert systems, speech recognition, and machine vision. AI is becoming more and more popular as a result of current initiatives like big data infrastructure and cloud computing. Leading businesses from a variety of sectors, such as Google, Microsoft, and Amazon (Software & IT); Bloomberg, American Express (Financial Services); Tesla and Ford (Automotive); have identified AI and cognitive computing as a key strategic driver and have started investing in machine learning to create more sophisticated systems. These leading companies have also given fledgling start-ups financial assistance in order to develop innovative new technologies.
The ML platform offers a wide range of benefits that support market expansion. However, it is anticipated that a number of platform criteria may restrict the market growth. One of the main challenges restricting the industry is the accuracy of these algorithms, which are occasionally undeveloped and immature. Precision is essential in the big data and machine learning industrial sectors. The method may have a small bug that causes wrong items to be generated. The owner of the manufacturing facility would see an outrageous rise in operating expenses rather than a decrease.
In order to guarantee the correctness of corporate operations utilising IoT platforms, a surge in the adoption of IoT (Internet of Things)-based applications is creating a tremendous need for machine learning (ML). As a result, IoT technology integration across several industry sectors contributes to enhanced consumer experiences. This element is predicted to accelerate the demand for machine learning as a service. The market for machine learning as a service is anticipated to expand as a result of the increased desire for small and medium-sized businesses (SMEs) to embrace AI-based solutions to increase productivity and efficiency.
During the continuing COVID-19 aftermath, the healthcare sector is creating new prospects for market participants in machine learning as a service. Along with the increasing use of cloud-based technologies by small and medium-sized businesses (SMBs), the desire to comprehend every individual uniquely and cater to their needs is also boosting the growth of the global market for machine learning as a service.
Vendors may offer specialised services and establish a strong brand by being able to segment diverse consumers. The machine learning as a service (MLaaS) market is anticipated to grow as a result of new application possibilities brought on by technical improvements, greater investment in the healthcare sector, and an increase in data from IoT systems.
Government regulations, a lack of qualified consultants available for implementing machine learning services, and over the course of the projection period, market obstacles might come from a lack of understanding and challenging compliance issues.
By Organization Size
By Industry Vertical
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