Machine Learning as a Service Industry to expand annually by 39.0% CAGR Prediction by AMR

Machine Learning as a Service

Machine Learning as a Service

Improved productivity, diversified application areas, increased customer satisfaction, and big data integration drive the artificial intelligence (AI) market.

PORTLAND, OR, UNITED STATES, November 24, 2021 /EINPresswire.com/ -- Machine learning is designed to incorporate the functionalities of artificial intelligence (AI) and cognitive computing involving a series of algorithms and is used to understand the relationship between datasets to obtain a desired output. Machine learning as a service (MLaaS) incorporates range of services that offer machine learning tools through cloud computing services.

The current business scenario has witnessed surge in adoption of machine learning as a service in the developed and the developing regions, owing to the technological innovations in product offerings, increased need to improve performance and operational efficiency, rise in demand for analytical systems, and increase in application areas among end users. Furthermore, factors such as growth associated with IoT, cognitive computing, neural networks, deep learning technologies, and artificial intelligence (AI) are expected to provide numerous growth opportunities. However, lack of trained professionals is expected to restrict the growth of the Machine Learning as a Service Industry during the forecast period.

According to a new report by Allied Market Research, titled, Machine Learning as a Service Market by Component, Organization Size, End-Use Industry, and Application: Global Opportunity Analysis and Industry Forecast, 2016-2023, the global machine learning as a service market was valued at $571 million in 2016, and is projected to reach at $5,537 million by 2023, growing at a CAGR of 39.0% from 2017 to 2023. BFSI segment possesses maximum potential, which is expected to create lucrative opportunities for industry players.

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The services segment dominated the Machine Learning as a Service Industry in 2016, with around 73% revenue share of the global market. This is attributed to the improved IT infrastructure and increased cloud-related expenditures to cater to the untapped markets among the developing nations.
The IT and telecom segment dominated the global Machine Learning as a Service Industry, with around 20% share in 2016, followed by BFSI segment. Furthermore, public sector segment is estimated to grow at the highest CAGR of 42.1%, owing to widespread adoption of advanced solutions and increased IT spending.

North America was the highest revenue contributor in 2016, accounting for around 43% share. However, Asia-Pacific is projected to grow at the highest CAGR of 45.5% during the forecast period.

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Key Findings of the Machine Learning as a Service Industry:
• The services segment dominated the global Machine Learning as a Service Industry in 2016.
• The IT and telecom segment estimated to provide significant growth opportunities to the key market players.
• The North America market is expected to be dominant, in terms of market size. However, Asia-Pacific and LAMEA is estimated to provide significant growth rate during the forecast period.

The report features the competitive scenario of the Machine Learning as a Service Industry and provides comprehensive analysis of the significant growth strategies adopted by major players. The key players operating in the market include Google Inc., SAS Institute Inc., FICO, Hewlett Packard Enterprise, Yottamine Analytics, Amazon Web Services, BigML Inc., Microsoft Corporation, Predictron Labs Ltd., and IBM Corporation.

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Allied Market Research (AMR) is a full-service market research and business-consulting wing of Allied Analytics LLP, based in Portland, Oregon. AMR provides global enterprises as well as medium and small businesses with unmatched quality of "Market Research Reports" and "Business Intelligence Solutions." AMR has a targeted view to provide business insights and consulting to assist its clients to make strategic business decisions and achieve sustainable growth in their respective market domains.

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