Designing Forecasting Pipelines for Production

AvansatNivel de competențe

Actualizat 12.2025

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

Descrierea cursului

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.

Cerințe prealabile

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

Descrierea cursului

Obține diploma de absolvire

Alătură-te celor peste .css-nklxlk{color:var(--wf-brand--main, #03EF62);}19 de milioane de cursanți și începe Designing Forecasting Pipelines for Production astăzi!

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Alătură-te celor peste 19 de milioane de cursanți și începe Designing Forecasting Pipelines for Production astăzi!