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...

Feature Scaling in Applied Machine Learning

If Model Training is the science of Machine Learning, Feature Engineering is the art of it. Having said that, every work of art has some standard tips and tricks...

The discipline(s) of Machine Learning Engineering

Before we dive into the discipline of Machine Learning Engineering, let’s see the below diagram from Google’s seminal paper called Hidden Technical Debt in...

Mind over Data – Towards Causality in AI

Data Centric AI vs Model Centric AI The structure of modern applied AI stands on the bricks of data. For a good AI/ML practice, a solid data practice is the key. This...