What is Machine Learning as a Service (MLaaS)?

There are high chances that you have come across many “as a service” offerings such as Software as a service and platform as a service, among others. While MLaaS may appear new, it is getting lots of attention due to the power of artificial intelligence and machine learning in human life in this age of technology. It entails providing tools and infrastructure to enable machine learning development and deployment at a specific price. Although it shares many similarities with software as a service (SaaS), MLaaS is more specific. Unlike the traditional approaches where you have to possess all the required infrastructure to enjoy the machine learning services, the new model allows you to have the services by simply paying for someone else’s infrastructure and resources as you go. MLaaS brings about the aspects of cost reduction, scalability, effectiveness, which are mainly handled by the professionals that you give money to for you to use their resources. It helps customers accelerate solutions as the economies of scale make it easy to set up and maintain the present solutions. 

MLaaS Service Providers

The MLaaS service providers are mainly large cloud computing service providers. These are companies such as Google, Amazon, and Microsoft, known for their Google Cloud Platform, Amazon Web Services, and Microsoft Azure. These companies are continuously setting up pace in cloud computing. Over the past few years, they have improved their machine tooling capabilities to keep up with the adoption of AI in almost every field. Some of the services you can get from these providers are virtual machines for training models, data versioning, hosting, model hosting, data labeling, and development environments. Amazon, for example, with its Lookout for Equipment, offers targeted industrial sectors, most of which have struggled in adopting machine learning because of a lack of expertise in the ML space.  

Getting started with MLaaS

Before getting started, there are a few considerations that must be taken into account. The first thing is often the ability of the chosen model to be tweaked or the flexibility of a particular framework or version. Also, consider the training you will need since some knowledge would be better used in running in-house infrastructure as this might be the cheaper alternative. An experienced person must undertake a thorough vetting to know the full potential of the chosen platform. 

As we enter the new age of technology, it is apparent that new MLaaS services and providers will enter the market. While some of them will not work as anticipated, most of them will be helpful to organizations that want to achieve. The complexity and dynamic nature of the modern world mean that only those with the capacity to respond perfectly to the changes will survive. MLaaS is a perfect solution to the problems in flexibility that we need today, but picking the right service will be crucial going forward.