Articles and Tutorials

Deploying Django to Google Cloud Platform - Compute Engine: Part 2

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:

  1. Pointing the Domain name to our GCP Instance.
  2. Setup SSL to use https instead of http.
  3. Redirect non-www to www.

Deploying Django to Google Cloud Platform - Compute Engine

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 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.

Fine-Tuning DistilBert for Multi-Class Text Classification using transformers and TensorFlow

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.

Custom Class for Glove Embeddings in a Scikit-learn Pipeline

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.