Category: Data Science
Explore your structured data using Natural Language with Azure Open AI
I saw a twitter quote saying, “prompt engineering is the new feature engineering.” It may be true given the rise of LLMs and especially ChatGPT. With precise...
Major approaches to Causal Inference
By nature, humans are inquisitive. We want to know the cause of everything that happens to us. In fact, the cause-effect phenomenon is central to Indian Philosophy in...
Will Generative AI replace Artists?
Ever since the emergence of AI, there has been a fear that it will replace humans. Right from a cobbler to Software Engineers, no one is immune to this fear. And this...
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...