The global machine learning as a service market stood at US$ 1,071.6 Mn in 2016. The study projects the global machine learning as a service market to expand at a CAGR of 38.4% during the period from 2018 to 2024 to reach US$ 19,566.4 Mn by 2024. The global machine learning as a service market, by geography, has been segmented into North America, Europe, Asia Pacific (APAC), Middle East and Africa (MEA), and South America. In the global machine learning as a service market, North America dominates the market in terms of revenue owing to the growing need for rapid integration of machine learning as a service with big data, Internet of Things (IoT), and other advanced technologies. Within North America, the U.S. and Canada are anticipated to drive the growth of machine learning as a service market.
The region is also a center for major service providers offering machine learning as a service including Google Inc., IBM Inc., Microsoft Corporation, Amazon Web Services Inc. and many others. In the global machine learning as a service market, Asia Pacific is anticipated to witness relatively faster adoption and hence the growth rate as compared with other regions. Within Asia Pacific, healthcare and life sciences end use application segment is projected to contribute the larger share of revenue due to the high rate of adoption of advanced analytics technology in this segment compared to other segment. Furthermore, Asia Pacific countries, in particularly Australia, China and India are prominently driving the growth in the region owing to the rapid technology developments in these regions. Machine learning as a service market in the Europe region is anticipated to witness relatively slower market growth during the forecast period from 2018 to 2024 owing to the implementation of general data protection regulation from 2018. However, across Europe countries including Germany, France and the U.K are anticipated to drive the growth of machine learning as a service market. As compared to North America and Europe, machine learning as a service market in the South America region is anticipated to witness relatively higher market growth. Countries including Brazil and Argentina among other countries are projected to witness high growth. Brazil, in South America dominated the machine learning as a service market in 2016, with majority share of revenue and is expected to continue its dominance over the forecast period from 2022 to 2032.
However, Argentina, is forecast to emerge as highest growth contributor among all other countries in South America. Machine learning as a service market in Middle East and Africa occupies a relatively smaller pie of the global machine learning as a service market. However, Middle East and South Africa is expected to register significantly healthy growth, with rate relatively closer to that of the South America region. Within Middle East and Africa counties including UAE, South Africa and Saudi are driving the growth. The growing demand for machine learning services is owing to growing cloud platform adoption in Saudi Arabia.
Key players profiled in the global machine learning as a service market include IBM Corporation, Google Inc., Amazon Web Services, Microsoft Corporation, BigMl Inc., FICO, Yottamine Analytics, Ersatz Labs Inc, Predictron Labs Ltd and H2O.ai. Other players include ForecastThis Inc., Hewlett Packard Enterprise, Datoin, Fuzzy.ai, Sift Science, Inc. among others.
Rest of North America
Rest of Europe
Rest of Asia Pacific
Middle East & Africa
Rest of Middle East & Africa
Rest of South America
InsightSLICE adopts a research methodology which is highly meticulous and comprehensive, yielding accurate research results. Our research methodology utilizes data triangulation model which helps in the precise collection and validation of information. Our set processes for problem solving and paid primary tools guarantee that any client requirement is met with utmost diligence and accuracy. Some of the primary components that are consequential to our research approach are:
Secondary Research or Desk Research
Distinctive Data Model
Secondary Sources include but are not limited to:
Past Published Research
Historical Data and Information
Primary Sources include but are not limited to:
InsightSLICE also leverages three types of data triangulation approaches as follows:
Data Source Triangulation
Extracting data and validation from multiple type of secondary and primary sources
Combining various methodologies to validate data inputs
Applying different theories to check credibility of data sets