Introduction

Up to this point in the book, we have studied several deep learning techniques that can be applied to solve specific problems in the NLP domain. Having knowledge of these techniques has empowered us to build good models and deliver high-quality performance. However, when it comes to delivering a working machine learning product in an organization, several other aspects need to be considered.

In this chapter, we will go through a practical project workflow when delivering a working deep learning system in an organization. Specifically, you will be introduced to the possible roles of various teams within your organization, building a deep learning pipeline and, finally, delivering your product in the form of SaaS.

General Workflow for the Development of a Machine Learning Product

Today, there are several ways of working with data science in an organization. Most organizations have a workflow that is specific to their environment. Some example workflows are as follows:

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