End-to-End machine learning is concerned with preparing your data, training a model on it, and then deploying that model. The goal of this two part series is to showcase how to develop and deploy an end-to-end machine learning project for an image classification model and using Transfer Learning.

Although there are plenty of other online resources which show you how to build your own models in detail, there are very few resources that dive into how to deploy these models. This article is a precursor to the second part, which will show the deployment steps. If you’re already familiar with building such a model and looking for ways on how to deploy it, then I would suggest to just skim over this post and check out Part-2.

Part-1 (this post): preparing the data and training an image classification model
Part-2: deploying the built model using Flask and Docker

#image-classification #data-science #deep-learning #machine-learning #end-to-end-ml