In the previous post, we saw how I trained an image classification model, starting from data preparation to training different iterations of the model, both using Convolutional Neural Networks(CNNs) and Transfer Learning to get a final model which classifies US dollar bills. If you haven’t already, I would suggest skimming through that post first and then come to this one.
Part-1 (previous post): preparing the data and training an image classification model
Part-2 (this post): deploying the model using Flask and Docker
After training the model, the next challenge was to showcase it. I didn’t want to spend too much…
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. …
The three main categories of Data Science are Statistics, Machine Learning and Software Engineering. To become a good Data Scientist, one needs to have a combination of all three in their quiver. In this post, I am going to talk about a Log Odds — an arrow from the Statistics category. When I first began working in Data Science, I was so confused about Log Odds. I would have questions like What is Log Odds, Why do we need them, etc.
When trying to understand any concept, I like to use the Divide and Understand strategy, i.e., break it into…
AWS (Amazon Web Services) has become a common name in the industry these days and if you are working with Python, chances are that you are already familiar with Jupyter (previously iPython) Notebooks. Although, if you are not familiar with Jupyter and you work with Python, then you are definitely missing on a very handy tool.
The reason for deploying Jupyter on AWS is to access and work with it from almost anywhere, just using a browser. So, let’s begin…
1. Login to your Amazon Management Console. If you don’t have an account yet, you can create one for free…
