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…
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
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Experimental evaluation of how the size of the training dataset affects the performance of a classifier trained through Transfer Learning.
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During my studies at JKU there was a task for preprocessing images for a machine learning project. It is necessary to clean the raw images…
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