Source: Algorithmia

There are a plethora of articles on Deep Learning (DL) or Machine Learning (ML) that cover topics like data gathering, data munging, network/algorithm selection, training, validation, and evaluation. But, one of the challenging problems in today’s data science is the deployment of the trained model in production for any consumer-centric organizations or individuals who want to make their solutions reach a wider audience.

Most of the time, energy and resources are spent on training the model to achieve the desired results, so allocating additional time and energy to decide on the computational resources to set up the appropriate infrastructure to replicate the model for achieving similar results in a different environment (production) at scale will be a difficult task. Overall, it’s a lengthy process that can easily take up months right from the decision to use DL to deploying the model.

This article tries to give a comprehensive overview of the entire process of deployment from scratch. Also, please feel free to comment below in case I miss something.