MLOps and Time Series Forecasting with MLflow and Prophet

Moving time series forecasting models into structured, trackable pipelines is an essential skill for modern data practitioners. This text-based course guides you through the fundamentals of time series modeling and shows you how to manage the complete model lifecycle using modern MLOps practices. You will begin with foundational concepts in time series analysis, exploring trends, seasonality, and specialized evaluation metrics before implementing forecasting algorithms. From there, you will learn how to organize, evaluate, and version your models systematically using MLflow. What you'll learn: - Understand core time series definitions, stationarity, seasonality, and evaluation metrics - Build accurate baseline and advanced forecast models using Prophet and sktime - Track parameters, metrics, and output plots systematically with MLflow Tracking - Organize and version forecasting artifacts within the MLflow Model Registry - Apply time series cross-validation and backtesting strategies to prevent data leakage - Configure reproducible machine learning workflows following modern MLOps standards The text begins with key terminology and theoretical foundations, progressing logically into practical code implementations and operational workflows. This course is designed for beginners interested in forecasting and machine learning operations, with no previous MLOps experience required. Begin reading today to bring structure and reproducibility to your time series projects.