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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...

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....

Musings on Data Quality

Introduction For a successful Machine Learning or Data Science practice, the following elements are key: Business Case Quality Data Skilled Teams Technology Risk...

Explainable Machine Learning with Azure Machine Learning

In the previous two articles, we took a dive into Explainable Machine Learning. The first one dealt with LIME and SHAP for a supervised machine learning setting. The...

Machine Learning Interpretability for Isolation forest using SHAP

In our previous article, we covered Machine Learning interpretability with LIME and SHAP. We introduced the concept of global and local interpretability. Moreover, we...

An Introduction to Interpretable Machine Learning with LIME and SHAP

Introducing Interpretable Machine Learning and(or) Explainability Gone are the days when Machine Learning models were treated as black boxes. Therefore, as Machine...

Motivating Entity Resolution for Data Science

Why Entity Resolution? Data is the new oil. Thus, analytical models are the new combustion engines. A combustion engine functions efficiently with good fuel. Similarly,...

Log Loss as a performance metric

Introduction to Log Loss Whenever we talk about performance metrics of the classification Machine Learning algorithms, the following names come to our mind: Accuracy...

Python Dedupe Library : Machine Learning to De-Duplicate Data

In Information systems, the biggest challenge faced by organizations is the quality of data. Hence, unclean, messy, and missing data is a common headache across the...