Drift in machine learning refers to shifts in data distribution that can compromise model performance. This issue arises when the patterns a model learned from historical data no longer apply to new data, leading to inaccurate predictions. Drift manifests in two main forms: data drift and concept drift. Data drift occurs when the distribution of input features changes, while concept drift involves a change in the relationship between input features and target outcomes. Both types can lead to model failure if not detected and managed. Detecting drift involves a systematic approach broken down into three stages: data collection and modeling, test statistic calculation, and hypothesis testing. First, we need to identify which data chunks to compare over defined time windows. After gathering the data, a test statistic quantifies differences between historical and new data. Finally, hypothesis testing determines if the observed changes signify drift. Methods for detecting drift can vary from monitoring model performance to analyzing incoming data directly. Drift can occur suddenly, gradually, or seasonally. For example, a sudden spike in online shopping during a holiday season reflects abrupt changes in consumer behavior. Conversely, gradual drift may happen as new product features gain traction over time. Detection methods range from simple threshold checks to more complex statistical tests, providing a framework to ensure machine learning systems remain reliable in evolving environments.