Skip to main content

Course

Designing Forecasting Pipelines for Production

AdvancedSkill Level

4.7+

Updated 12/2025

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

Start Course for Free

PythonMachine Learning4 hr16 videos53 Exercises4,000 XPStatement of Accomplishment

Create Your Free Account

or

By continuing, you accept our Terms of Use, our Privacy Policy and that your data is stored in the USA.

Loved by learners at thousands of companies

Training 2 or more people?

Try DataCamp for Business

Course Description

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.

Prerequisites

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

1

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

Forecasting project architecture

Benefits of experimentation frameworks

Reviewing the input data

Making an API request

Preparing and visualizing the data

Interpreting time series visualizations

Working with a forecast object

Forecasting with ML Models

Evaluating forecast performance

Visualizing forecast performance

2

Experimentation

Discover the fundamentals of experimentation, including backtesting, evaluation, and model registration using MLflow!

Experimentation

Forecast evaluation & experimentation

Exploring experimentation process

Workflow of experimentation

Backtesting

Defining the forecasting models

Training models with backtesting

Visualizing forecast results

Model Scoring

Transforming backtesting output

Building a model leaderboard

Model Registration with MLflow

Logging tuned models

Searching experiment results

3

Setting Automation

Learn how to build automated forecasting pipelines that refresh data and predictions daily. You'll set up ETL processes, register models with MLflow, and orchestrate everything with Airflow. Create a production-ready system with data validation and logging to monitor pipeline health.

Pipeline architecture

Defining the forecasting pipeline

Fitting the model

Registering the model

Data ingestion

Data parity checks

Defining the schema

Performing data validation

Setting up the DAG

Creating the DAG

Defining pipeline execution order

Logs

Monitoring ETL logs

Monitoring forecasting logs

4

From Deployment to Production

Discover the essentials of production deployment, from monitoring the pipeline health to detecting model drift. You'll learn best practices for reproducibility, scaling, and maintaining forecasting systems in real-world environments.

Pipeline Deployment to Production

What could go wrong?

Monitoring vs Observability

Pipeline Monitoring

Categorizing API risks

Building a mitigation plan

Model Drift

Calculating a threshold and moving averages

Identifying model drift

Best practices

Development best practices

Scaling forecasting pipelines

Congratulations!

Designing Forecasting Pipelines for Production

Course
Complete

Earn Statement of Accomplishment

Add this credential to your LinkedIn profile, resume, or CV
Share it on social media and in your performance reviewEnroll Now

Don’t just take our word for it

*4.7

from 66 reviews

82%

15%

2%

2%

0%

Sort by

Venkata SubbaRao

6 weeks ago

Dedy

2 months ago

Pedro

3 months ago

Sogol

3 months ago

JiYoung

3 months ago

Danielle L.

3 months ago

Dedy

Pedro

Sogol

FAQs

What will I learn in this course?

You will learn how to design a forecasting pipeline for production, including data automation, model experimentation, deployment, and monitoring using a real-world electricity demand example.

How will this course help me in my career?

You will gain practical skills to move forecasting models from experimentation to production, a capability highly valued in data science and ML engineering roles.

What is a forecasting pipeline, and why is it important?

A forecasting pipeline automates recurring forecasting tasks at scale, ensuring consistency and reducing manual effort in production environments.

Join over 19 million learners and start Designing Forecasting Pipelines for Production today!

Create Your Free Account

or

By continuing, you accept our Terms of Use, our Privacy Policy and that your data is stored in the USA.

Grow your data skills with DataCamp for Mobile

Make progress on the go with our mobile courses and daily 5-minute coding challenges.