After doing these courses, I feel confident creating professional visualizations and dashboards
Shoebur RahmanProcurement Specialist, Giz Bangladesh
课程介绍
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.
先修要求
课程大纲
课程大纲
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 model50 经验值
- Forecasting project architecture50 经验值
- Benefits of experimentation frameworks50 经验值
- Reviewing the input data50 经验值
- Making an API request100 经验值
- Preparing and visualizing the data100 经验值
- Interpreting time series visualizations50 经验值
- Working with a forecast object50 经验值
- Forecasting with ML Models100 经验值
- Evaluating forecast performance100 经验值
- Visualizing forecast performance100 经验值
2
Experimentation
Discover the fundamentals of experimentation, including backtesting, evaluation, and model registration using MLflow!
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.
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.
Designing Forecasting Pipelines for Production
课程完成
