MLOps with MLflow: Creating Time-series Models & Evaluating Models

MLflow integrates with Prophet, a powerful time-series model that considers seasonal effects. MLflow provides a variety of model evaluation capabilities, empowering you to thoroughly assess and analyze model performance. First, you will use Prophet in combination with MLflow for time-series forecasting. Integrating Prophet with MLflows tracking capabilities, you will seamlessly manage and evaluate your time-series models. Running the Prophet model and viewing metrics will allow you to assess its forecasting performance. Cross-validation will enhance the evaluation process, ensuring reliability across different temporal windows. Then, you will use MLflow to evaluate machine learning (ML) models effectively. MLflows evaluation capabilities, including Lift curves, Receiver Operating Characteristic-Area Under the Curve (ROC-AUC) curves, precision-recall curves, and beeswarm charts, provide valuable insights into model behavior and performance. Finally, you will use MLflow to configure thresholds for model metrics and only validate those models which meet this threshold.