Detecting Model Drift in Time Series Forecasting with Python

Time series forecasting models often lose accuracy as real-world patterns change, leading to silent failures in production. Understanding when and why your models degrade is essential for maintaining reliable predictions in any data-driven system. This written course guides you through the fundamentals of model drift detection, helping you implement robust monitoring strategies for temporal data. What you will learn: 1. Understand the core differences between data drift, concept drift, and covariate shift. 2. Implement statistical tests and distance metrics using Python to measure distribution changes. 3. Build monitoring pipelines to detect performance degradation in production. 4. Apply modern MLOps concepts to trigger retraining workflows. 5. Practice writing clean, typed Python code to automate drift detection checks. Starting with key terminology and foundational definitions, you will progress through practical statistical methods and automated monitoring workflows. This course is designed for beginners with basic Python knowledge and no prior drift detection experience. Start reading today to keep your forecasting models accurate and reliable.