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Machine Learning as a Service


How to deploy ML Models as APIs without going nuts

Often, the most convenient way to deploy a machine model is an API. It allows accessing it from various programming environments and also decouples the development and deployment of the models from its use.

However, building an good API is hard. It involves many nitty-gritties and many of them need to repeated every time an API is built. It requires understanding of some web framework, worrying about data validation, authentication and deploying etc. Also, it is very important to have a client library so that the API can be easily accessed. If you ever plan to use it from Javascript directly, then you need to worry about cross-origin-resource- sharing etc.

In this talk demonstrates how deploying machine learning models an APIs can be made fun by using right programming abstractions and tools.


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