Best Monitoring Practices for Data Science Models 

Best Monitoring Practices for Data Science Models   So you’ve got your data science model up and running. You’ve done everything right to get here and you’re super excited about your model. Your model was trained on the best and most representative data, along with the most robust set of features and the highest accuracy that one has ever seen on test datasets. But even with the best training and deployment practices, data science models can easily drift away from optimal performance and start causing losses for the company.   Most of these problems arise from unseen circumstances or situations… Read More


Understanding the Confusion Matrix for Model Evaluation & Monitoring

Understanding the Confusion Matrix for Model Evaluation & Monitoring   Anyone can build a machine learning (ML) model with a few lines of code, but building a good machine learning model is a whole other story. What do I mean by a GOOD machine learning model? It depends, but generally, you’ll evaluate your machine learning model based on some predetermined metrics that you decide to use. When it comes to building classification models, you’ll most likely use a confusion matrix and related metrics to evaluate your model. Confusion matrices are not just useful in model evaluation but also model monitoring… Read More


An Introduction to Ethics in AI

An Introduction to Ethics in AI Background of Artificial Intelligence Artificial Intelligence (AI) has been a hot topic in the twenty-first century. It’s become so prevalent that there’s a need for over a million AI engineers worldwide, YouTube created a nine-video series on AI, and Elon Musk started a company called Neuralink in response to his concerns around AI. AI has almost doubled in interest over the past five years according to Google Trends, but has been around since the 1950’s — Norbert Wiener theorized that all intelligent behavior was the result of feedback mechanisms and this very idea influenced much of… Read More