Machine learning as a service (MLaaS) market combines a wide-ranging series of services, solutions and techniques that are interconnected closely to artificial intelligence (AI), which performs statistical analysis of input data to comprehend its current and future performance. Machine learning makes use of huge amount of input data to deliver enhanced analytical output while elevating workflow for diverse industry verticals. Machine learning as a service incorporates range of services that offer machine learning tools through cloud computing services.
Recommendation systems that are used across a broad range of industries, which are mostly notable online shopping sites help the organizations to get deeper insights about the customer’s behavior by helping them to discover new and relevant offers. This leads to a stronger customer relationship and generates higher sales for the business, and is eventually the major factor that is driving the growth of the market. However, machine learning as a service (MLaas) was the ‘cold start’ for many organizations, wherein they lack with the historical data that is needed to provide recommendations. This is the most important aspect that is limiting the market to grow. On the flip side, some organizations need to make important decisions during their real time wherein machine learning models provide the best predictions and is expected to grow tremendously in the upcoming days.
Some of the major players operating in the global Machine Learning as a Service (MLaaS) market are Google, Amazon Web Services (Amazon Web Services, Inc.), IBM Corporation, Microsoft Corporation, SAS Institute Inc., BigML, Inc., DataRobot, Inc., FICO (Fair Isaac Corporation), Yottamine Analytics, LLC, and Algolytics among the others.
SAS Institute Inc. supports end-to-end machine learning process with a comprehensive visual and programming interface that handles all tasks in the analytical lifecycle. This service has benefits like –
1) Flexible, approachable visual environment for analytics.
2) Highly scalable in memory analytical processing.
3) Model assessment and scoring.
4) Accessible and cloud ready

• By Component
o Software tools
o Cloud Based APIs
o Web Based APIs

• By Application
o Network Analytics
o Predictive Maintenance
o Augmented Reality
o Marketing and Advertising
o Risk Analytics and Fraud Detection
o Others

• By Organization
o SMBs
o Large Enterprises

• By Deployment Type
o Cloud
o On Premise

• By End User
o Manufacturing
o Healthcare
o Retail
o Transportation
o Government
o Telecom
o Others

• By Region
o Asia Pacific
o Europe
o North America
o Rest of the World

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