We're excited to announce that we have recently launched our own startup at www.quizwhiz.ai. It is a tool that lets you generate Multiple Choice Question and Answers, using Machine Learning. Looking for early adopters, so please do check it out and provide any feedback. Thanks!
This is Part 2 of the two-part series on how to deploy a Django application to Google Cloud Platform (GCP). In Part 1, I discussed how to set up a Virtual Machine using GCP's Compute Engine, and deploy a Django application to it, which was then accessible through just an IP address.
In this tutorial, I will continue from where we left off, and demonstrate the following 3 things:
In the past, whenever I wanted to launch a Django website on a Linux server, I would follow the steps outlined in the famous Corey Shafer's tutorial on launching a Django Application on Linode.com. However, recently I've had to launch a Django application on the Google Cloud Platform, and even though most of the steps are the same, I had to do a lot of trial and error to figure out the exact steps required to achieve the same. And this is what I will show you in this tutorial.
In this tutorial, we will be fine-tuning a DistilBert model for the Multiclass text classification problem using a custom dataset and the HuggingFace's transformers library.
In this tutorial, I'm going to share with you how I implemented Glove Vector Embeddings for a text classification task that uses the Scikit-learn Pipeline. I will explain how Pipelines work in general and how to create a custom class in Scikit-learn for Glove Vector Embeddings while going through an example classification task using a dataset from Kaggle.
Please check out our startup at QuizWhiz.ai