Writing Work

Here are a few of my favourite articles, blogs, and tutorials that have been published so far.

Data-Centric Approach vs Model-Centric Approach in Machine Learning

Code and data are the foundations of the AI system. Both of these components play an important role in the development of a robust model but which one should you focus on more? In this article, we’ll go through the data-centric vs model-centric approaches, and see which one is better, we would also talk about how to adopt data-centric infrastructure.

How to use MLflow to Track and Structure Machine Learning Projects?

Machine Learning is an expensive, experimental process. Every step has to be meticulously planned, and every input is required to have a meaningful effect on the output. The process can take weeks or months to complete, but once it gets rolling, there are no signs of stopping it for a good reason: Machine Learning algorithms are by nature unpredictable and can change course at any moment because they are "trained" on new data points that weren't present before.

MLOps vs. DevOps vs. ModelOps

It’s the age of automation, and it’s not just in manufacturing. Nowadays, systems of all kinds are being automated to reduce human error and increase efficiency. Systems like DevOps, ModelOps, and MLOps are all driven by data. So what is the difference between them? This article will go through their life cycles, concepts, best tools, and platforms. You can skip directly to the part you're interested in.

‍

ML from Research to Production – Challenges, Best Practices and Tools [Guide]

Taking machine learning or AI into production takes a lot of patience, effort, and resources. AI models are great for predicting all sorts of things, from what movie you’ll like to whether your cat will scratch the furniture. But in most cases, AI models have a hard time making it into production.

In this article, we’ll discuss why it’s hard to get models to production, how you can take your machine learning experiments from research to production, and things to consider after your model is deployed to production.

Challenges in Deploying Machine Learning Models

Machine learning is a booming industry that has brought incredible advancements in various fields. From small startups to large organizations, everyone is adopting ML and AI for their business and applications. Many organizations are looking to deploy machine learning models, but challenges arise when maintaining good model performance. These challenges are often met with many solutions, including data augmentation, transfer learning, and much more.

Model Debugging Strategies – Machine Learning Guide

You can’t deploy software based on a buggy model. Debugging is a big part of model development and deployment in machine learning.

Is there one silver-bullet strategy that works for all types of models and applications? Not really, there are plenty of different strategies for model debugging, and we’re going to explore 10 of them in this article.

Playing Chrome’s Dinosaur Game using OpenCV

We are going to use Python and OpenCV to play the dinosaur game. Here I’m using Windows, and my webcam for the camera feed, but you can use most of the code for Linux and MacOS as well.

We are basically going to play the Chrome Dinosaur game using hand movements from the camera feed.

Wireless debugging in Android 11

Android 11 developer preview came with lots of new and amazing features focusing on 5G integration, scoped storage, improved privacy and security, and more. One of my personal favorite feature is Wireless debugging. It is a new method for deploying and debugging your app wirelessly from your workstation.

6 Creative Ideas for CSS Link Hover Effects

Creating CSS link hover effects can add a bit of flair to an otherwise bland webpage. If you’ve ever found yourself stumped trying to make a slick hover effect, then I have six CSS effects for you to take and use for your next project.

Getting started with competitive coding

This is a question that has haunted me for years, and I could never begin for a long time. Finally, it turned out to be a simple lesson: “start by doing”, and that’s all that is required. The only problem in this statement is what exactly to start with.