What is Machine learning as a service (MLaaS)?

  • Home
  • Blogs
  • What is Machine learning as a service (MLaaS)?
What is Machine learning as a service (MLaaS)?
What is Machine learning as a service (MLaaS)?
August 07, 2019

Earlier the development of a product into full-fledged service(s) in the cloud has seen the rise of new services such as Platform as a service (PaaS), Infrastructure as a service (IaaS) and Software as a service (SaaS). Core Technologies Services, Inc. finds that there is a new contender added to the Cloud market space and this is known as Machine learning as a service (MLaaS).

What is MLaaS?

Machine learning as a service (MLaaS) is a range of services that offer machine-learning tools as part of Cloud Computing Services. These services from providers offer tools that include data visualization, APIs, face recognition, natural language processing, predictive analytics and deep learning. The actual computation is handled by the provider’s data centers.

The lure of these services is, just like any other cloud service, customers can quickly start with machine learning without having to install software or provision their own servers. These services are offered by many cloud providers like Microsoft, Amazon, and IBM and include learning tools. Often MLaaS is offered on a limited trial basis for developers to evaluate before committing to a platform so that they can be well versed with it.


MLaaS is a set of services that are generic machine learning, often ready-made tools that can be adapted by any organization as a part of their working needs. The MLaaS algorithms are used to find a pattern in data and the key is in the fact that the users do not need to handle the actual computation. Additionally, MLaaS is also the only full-stack AI platform that combines systems ranging from the mobile application, enterprise information, industrial automation, and control.

Uses and benefits of Machine learning as a service (MLaaS) 

CoreIT finds MLaaS used in processes such as risk analytics, fraud detection, manufacturing, supply chain optimization and a lot more other areas. It offers freedom building in-house infrastructure from scratch and management and storage of data becomes easy. It has a synergistic value to engage data with cloud and can revolutionize a paradigm of machine learning for the specific result.