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Kavita Rana

Kavita Rana

Data Science Content Intern at NannyML

Prevent Failure of Product Defect Detection Models: A Post-Deployment Guide
Use CasesRoot Cause Analysis
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Prevent Failure of Product Defect Detection Models: A Post-Deployment Guide

This blog dissects the core challenge of monitoring defect detection models: the censored confusion matrix. Additionally, I explore how business value metrics can help you articulate the financial impact of your ML models in front of non-data science experts.

Why Relying on Training Data for ML Monitoring Can Trick You
ML Monitoring 101
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Why Relying on Training Data for ML Monitoring Can Trick You

The most commonly repeated mistake while choosing a reference dataset is using the training data. This blog highlights the drawbacks of this decision and guides you on selecting the correct reference data.

A Comprehensive Guide to Univariate Drift Detection Methods
Root Cause Analysis
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A Comprehensive Guide to Univariate Drift Detection Methods

Discover how to tackle univariate drift with our comprehensive guide. Learn about key techniques such as the Jensen-Shannon Distance, Hellinger Distance, the Kolmogorov-Smirnov Test, and more. Implement them in Python using the NannyML library.

Stress-free Monitoring of Predictive Maintenance Models
Use Cases
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Stress-free Monitoring of Predictive Maintenance Models

Prevent costly machine breakdowns with NannyML’s workflow: Learn to tackle silent model failures, estimate performance with CBPE, and resolve issues promptly.

Multivariate Drift Detection: A Comparative Study on Real-world Data
Root Cause Analysis
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Multivariate Drift Detection: A Comparative Study on Real-world Data

This blog introduces covariate shift and various approaches to detecting it. It then deep-dives into the various multivariate drift detection algorithms with NannyML on a real-world dataset.