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...
Analyzing Machine Learning Retraining Pipelines
A key requirement to successful MLOps practice is the ability to build and maintain reliable and repeatable training pipelines, also called Machine Learning Retraining...
Introducing Machine Learning Infrastructure (on Azure)
Functional requirements of a Machine Learning Infrastructure We have been discussing Data Science, Machine Learning System Design and MLOps for a while in our articles....
Batch Inferencing in Azure ML using Managed Online Endpoints
Batch Inferencing with Azure ML is a complex affair. It entails creating a compute cluster, creating a parallel run step and running the batch inferencing pipeline. With...
MLOps maturity of an Organization
What is MLOps? MLOps, like DevOps, is a discipline to put ML models into production. It is the journey of an ML algorithm from notebooks to a production environment....