In the midst of this pandemic, what is allowing us unprecedented flexibility in making faster technological advancements is the availability of various competent cloud computing systems. From delivering on-demand computing services for applications, processing and storage, now is the time to make the best use of public cloud providers. What’s more, with easy scalability there are no geographical restrictions either.

Machine Learning systems can be indefinitely supported by them as they are open-sourced and within reach now more than ever with increased affordability for businesses. In fact, public cloud providers are increasingly helpful in building Machine Learning models. So, the question that arises for us is – what are the possibilities for using them for deployment as well?

What do we mean by deployment?

Model building is very much like the process of designing any product. From ideation and data preparation to prototyping and testing. Deployment basically is the actionable point of the whole process, which means that we use the already trained model and make its predictions available to users or other systems in an automated, reproducible and auditable manner.

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