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Shoebur Rahman구매 담당자, Giz Bangladesh
강의 설명
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 과목
커리큘럼
강의 개요
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 XP
- Forecasting project architecture50 XP
- Benefits of experimentation frameworks50 XP
- Reviewing the input data50 XP
- Making an API request100 XP
- Preparing and visualizing the data100 XP
- Interpreting time series visualizations50 XP
- Working with a forecast object50 XP
- Forecasting with ML Models100 XP
- Evaluating forecast performance100 XP
- Visualizing forecast performance100 XP
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
강의
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