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
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.
I am looking for editors/curators to help with branches of the tree. Please send me an email if you are interested.