Designing Forecasting Pipelines for Production

上級スキルレベル

更新日 2025/12

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

コース説明

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.

前提条件

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

コース説明

修了証明書を取得

.css-nklxlk{color:var(--wf-brand--main, #03EF62);}19百万人を超える学習者と共にDesigning Forecasting Pipelines for Productionを始めましょう！

無料アカウントを作成

DataCamp for Mobileでデータスキルを磨きましょう

19百万人を超える学習者と共にDesigning Forecasting Pipelines for Productionを始めましょう！