Designing Forecasting Pipelines for Production

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업데이트됨 2025. 12.

Learn how to design, automate, and monitor scalable forecasting pipelines in Python.

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Learn how to design, automate, and monitor scalable forecasting pipelines in Python. This advanced course walks you through the entire production workflow - from sourcing data and training models to deployment and monitoring - using tools like MLflow and Airflow.You'll start by connecting to live data sources and building your first forecast with U.S. electricity demand data. Next, you'll discover experimentation fundamentals, including backtesting, evaluation, and model registration using MLflow.Then you'll build automated forecasting pipelines with ETL processes, model registration, and Airflow orchestration. Finally, you'll learn production deployment essentials, including monitoring pipeline health, detecting model drift, and maintaining forecasting systems in real-world environments.

선수 조건

Introduction to Apache Airflow in Python Introduction to MLflow Time Series Analysis in Python

General Architecture

Learn how to connect to live data sources and prepare time series data for forecasting. You’ll pull hourly electricity demand data from the U.S. EIA API and build your first forecast.

Productionizing your forecast model

50 XP

Forecasting project architecture

50 XP

Benefits of experimentation frameworks

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19백만 명 이상의 학습자와 함께 Designing Forecasting Pipelines for Production을(를) 시작하세요!