Machine Learning as a Service (MLaaS) Market is Projected to Register at a CAGR of 36.2% by 2030
Market Research Future Insights
According to MRFR analysis, the global Machine Learning as a Service (MLaaS) market is expected to register a CAGR of 36.2% from 2022 to 2030 and hold a value of over $302.66 billion by 2030.
Machine Learning as a Service (MLaaS) is a type of cloud service that enables developers and businesses to leverage the power of machine learning without the need for in-house expertise or infrastructure. MLaaS providers offer a variety of services such as pre-trained models, algorithms, and APIs that can be used to build, deploy, and manage machine learning applications. Due to the pandemic, many organizations have shifted to remote work and digital transformation, which has led to an increased demand for MLaaS solutions that can automate various business processes, improve efficiency, and support decision-making. Additionally, the healthcare industry has also seen a significant increase in the use of MLaaS solutions, such as for analyzing medical imaging and predicting the spread of the virus.
Key Players
- BigML
- Microsoft
- IBM
- Amazon Web Services
- AT&T
- Yottamine Analytics
- Ersatz Labs Inc.
- Sift Science Inc.
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Market Segmentation
The Global Machine Learning as a Service (MLaaS) market has been segmented into components, applications, and deployment.
Based on the component, the market has been segmented into Software tools, Cloud APIs, and Web-based APIs.
Based on the application, the market has been segmented into Network analytics, Predictive maintenance, and Augmented reality.
Based on the deployment, the market has been segmented into Cloud and On-Premise.
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Regional Analysis
The North American region holds the largest share in the machine learning as a service (MLaaS) industry, due to the increasing growth of start-ups in countries like Canada and the U.S. These countries are the hub of various small and large start-ups, which increases the demand for MLaaS in the North American region.
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