Global MLaaS Platform Market Growth (Status and Outlook) 2024-2030

Global MLaaS Platform Market Growth (Status and Outlook) 2024-2030

Machine Learning as a Service (MLaaS) is machine learning technology that is conscripted from another business. This is similar to SaaS (Software as a Service) or PaaS (Platform as a Service), meaning you use the services of a company, rather than wholly create your own.



Using MLaaS means the computation is handled outside of your company – you pay for the machine learning services you need, and data storage may be handled on your personal servers or in the cloud (usually offered with MLaaS providers).



MLaaS software can be used to automatically analyze online product reviews and respond to customers, streamline processes by automating tasks like ticket routing, and even power self-driving cars.

The global MLaaS Platform market size is projected to grow from US$ 2313.1 million in 2023 to US$ 8399.4 million in 2030; it is expected to grow at a CAGR of 20.2% from 2024 to 2030.

LPI (LP Information)' newest research report, the “MLaaS Platform Industry Forecast” looks at past sales and reviews total world MLaaS Platform sales in 2023, providing a comprehensive analysis by region and market sector of projected MLaaS Platform sales for 2024 through 2030. With MLaaS Platform sales broken down by region, market sector and sub-sector, this report provides a detailed analysis in US$ millions of the world MLaaS Platform industry.

This Insight Report provides a comprehensive analysis of the global MLaaS Platform landscape and highlights key trends related to product segmentation, company formation, revenue, and market share, latest development, and M&A activity. This report also analyzes the strategies of leading global companies with a focus on MLaaS Platform portfolios and capabilities, market entry strategies, market positions, and geographic footprints, to better understand these firms’ unique position in an accelerating global MLaaS Platform market.

This Insight Report evaluates the key market trends, drivers, and affecting factors shaping the global outlook for MLaaS Platform and breaks down the forecast by Type, by Application, geography, and market size to highlight emerging pockets of opportunity. With a transparent methodology based on hundreds of bottom-up qualitative and quantitative market inputs, this study forecast offers a highly nuanced view of the current state and future trajectory in the global MLaaS Platform.

United States market for MLaaS Platform is estimated to increase from US$ million in 2023 to US$ million by 2030, at a CAGR of % from 2024 through 2030.

China market for MLaaS Platform is estimated to increase from US$ million in 2023 to US$ million by 2030, at a CAGR of % from 2024 through 2030.

Europe market for MLaaS Platform is estimated to increase from US$ million in 2023 to US$ million by 2030, at a CAGR of % from 2024 through 2030.

Global key MLaaS Platform players cover MonkeyLearn, BigML, TensorFlow, IBM Watson and Apache Mahout, etc. In terms of revenue, the global two largest companies occupied for a share nearly % in 2023.

This report presents a comprehensive overview, market shares, and growth opportunities of MLaaS Platform market by product type, application, key players and key regions and countries.

Segmentation by type
Natural Language Processing (NLP)
Image and Video Analysis
Computer Vision

Segmentation by application
Retail
Aviation
Finance
IT
Medical Health Care
Government
Others

This report also splits the market by region:
Americas
United States
Canada
Mexico
Brazil
APAC
China
Japan
Korea
Southeast Asia
India
Australia
Europe
Germany
France
UK
Italy
Russia
Middle East & Africa
Egypt
South Africa
Israel
Turkey
GCC Countries

The below companies that are profiled have been selected based on inputs gathered from primary experts and analyzing the company's coverage, product portfolio, its market penetration.
MonkeyLearn
BigML
TensorFlow
IBM Watson
Apache Mahout
Azure
Google Cloud ML

Please note: The report will take approximately 2 business days to prepare and deliver.