ML model drift is the phenomenon whereby a machine learning model's performance on a given task deteriorates over time. This can happen for a variety of reasons, including changes in the underlying data distribution, changes in the model's environment, or even changes in the way the model is used..

Most people often mix the terms Data Science, AI, Machine Learning, and Data Mining or use some of them interchangeably. But all of these terms have their separate meaning and stream. Data has always been a central element of any business to make precise decisions from granular data. Even though all of these terminologies are somehow interrelated to data, each has its characteristics. This article will give you a clear understanding of all these four terminologies and their differentiation..

This article will discuss how supply chains are being improved through the use of innovative technologies before highlighting five uses of artificial intelligence and machine learning in supply chains.

When you finish reading, you’ll understand why many industry analysts have described A.I. technologies as disruptive innovations that have the potential to alter and improve operations across entire supply chains..