Category: Data Science

Major swimlanes of Causality

In our article on Causal Inference in Observational Studies, we studied the motivation for the same using the example of Simpson’s Paradox. It highlights the...

An Introduction to Modeling Mindsets

A mathematical model is a representative of a real-world phenomenon. We seek to understand the world and take action accordingly using models. This includes everything...

Natural Language Data Augmentation using nlpaug

Machine Learning Algorithms learn from data. And, with the advent of Deep Learning techniques, this is more profound than ever. Given large compute resources at their...

Interpretations and Definitions of Probability

As far as the laws of mathematics refer to reality, they are not certain, and as far as they are certain, they do not refer to reality~Albert Einstein The world we see...

Machine Learning System Design is a mindset

I was interviewing a candidate recently for a Machine Learning Engineering role. I asked some questions about Machine Learning Deployment and Drift. In general, the...

The 3 V’s of MLOps

Anyone familiar with Data Analytics/Big Data world may be aware of the 3 V’s of Big Data. They are Volume, Velocity and Variety (there is a 4th V called Veracity)....

Data and Model Validation in Machine Learning Training Pipelines

Why Data and Model Validation? A Machine Learning application is a piece of Software, the end of the day. Hence, all the maxims applicable to software engineering are...

Why Causal Inference in Observational Studies?

Experimental Studies vs Observational Studies Causal Inference is concerned with a very specific kind of prediction problem: predicting the results of an action,...

Introducing Decision Intelligence and CDD

Every conscious moment of life is a Decision to be made. Starting from the amount of toothpaste to be used in the morning to the book to be read before bedtime, our...

Test in Production with Azure ML Managed Online Endpoints

Why test in production? A Machine Learning lifecycle starts with experiments, moves on to building training pipelines and finally ends with deployment. So, are you done...