Machine learning as a service refers to a range of services which offer machine learning tools as a part of cloud computing services, these tools include data visualization, APIs, face recognition, natural language processing, predictive analytics and deep learning. The main advantage of machine learning as a service is that customer can get started quickly with machine learning without downloading and installing any software at their end. Machine learning as a service delivers developers services which include data modeling APIs, machine learning algorithms, data transformations and predictive analytics.
The increased volume of data and diverse data to be handled qualitatively, are major factors driving the growth of Machine Learning as a Service (MLaaS) market. The innovations in automation technologies and increasing adoption of cloud-based systems is boosting the growth of machine learning as a service market. Apart from it availability of data storage at a cheaper rate and adoption of Internet of Things (IoT) is also driving the Machine Learning as a Service (MLaaS) market. The study indicates that lack the historical data is one of the major restraining factor in the growth of machine learning as a service (MLaaS) market.
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Machine learning as a Service (MLaaS) market is segmented on the basis component, application, organization size, deployment and end-user. It has been observed that adoption of cloud technologies has become worldwide, this has resulted in increase of demand for cloud-based machine learning as a service (MLaaS).
The global Machine Learning As A Service (Mlaas) Market is expected to grow at USD ~4630 Million by 2022, at ~40% of CAGR between 2016 and 2022.
The prominent players in the machine learning as a service (MLaaS) Market are- Google (U.S.), BigML (U.S.), Microsoft (U.S.), IBM (U.S.), Amazon Web Services (U.S.), AT&T (U.S.), Fuzzy.ai (Canada), Yottamine Analytics (U.S.), Ersatz Labs, Inc. (U.S.), and Sift Science, Inc. (U.S.) among others
To generate a comprehensive understanding of the market necessitated by the complexity and the nascent stage of the market, the Machine learning as a Service (MLaaS) market has been segmented by
- Based on components, the market has been segmented by software tools, cloud based APIs, web based APIs, and others.
- Based on applications, the market has been segmented by network analytics, predictive maintenance, augmented reality, marketing and advertising, risk analytics and fraud detection, and others
- Based on organization size, the market has been segmented by SMBs, and large enterprises.
- Based on deployment type, the market has been segmented by cloud, and on premise.
- Based on end user, the market has been segmented by manufacturing, BFSI, healthcare, retail, transportation, government, telecom, and others.
- Based on regions, the market has been segmented by North America, Europe, Asia Pacific, Middle East and Africa.
North America led by the U.S. Accounts for the largest share of the global machine learning as a service (MLaaS) market followed by Europe owing to the rapid uptake of new technology, presence of global players and others. However the market is expected to shift towards the developing regions such as Asia Pacific led by Japan, China and India. The Middle East and Africa market is expected to be dominated by Gulf economies of Saudi Arabia, UAE, Kuwait, and Qatar.
January, 2018 – Google LLC, an American Multinational Technology Company launched a new service which aims at businesses by converting machine leaning as a practical tool. This service is called Cloud Auto ML which aims at enterprises lacking expertise in data science by providing machine learning or AI as service.
December, 2017 – Oracle Corporation, an American multinational computer technology corporation, is integrating machine learning across all of its services through the cloud platform. By offering through cloud it makes self-driving, repairing and securing which helps in saving operational costs. This will benefit Oracle by applying AI and machine learning through all of their services which will help customers by lowering cost, reducing risk and to help in making smarter decisions.
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- Machine learning as a service (MLaaS) vendors
- Resellers and distributors
- Technology investors
- System Integrators
- Government Organizations
- Research/Consultancy firms
- Network solution providers
- Independent software vendors
- Managed Security Service Providers (MSSPs)
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