课程

Designing Forecasting Pipelines for Production

高级技能水平

更新时间 2025年12月

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

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PythonMachine Learning4 小时16 视频53 练习4,000 经验值成就声明

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课程描述

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.

先决条件

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

50 经验值

Forecasting project architecture

50 经验值

Benefits of experimentation frameworks

50 经验值

Reviewing the input data

50 经验值

Making an API request

100 经验值

Preparing and visualizing the data

100 经验值

Interpreting time series visualizations

50 经验值

Working with a forecast object

50 经验值

Forecasting with ML Models

100 经验值

Evaluating forecast performance

100 经验值

Visualizing forecast performance

100 经验值

2

Experimentation

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

Experimentation

50 经验值

Forecast evaluation & experimentation

100 经验值

Exploring experimentation process

50 经验值

Workflow of experimentation

100 经验值

Backtesting

50 经验值

Defining the forecasting models

100 经验值

Training models with backtesting

100 经验值

Visualizing forecast results

100 经验值

Model Scoring

50 经验值

Transforming backtesting output

100 经验值

Building a model leaderboard

100 经验值

Model Registration with MLflow

50 经验值

Logging tuned models

100 经验值

Searching experiment results

100 经验值

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

50 经验值

Defining the forecasting pipeline

100 经验值

Fitting the model

100 经验值

Registering the model

100 经验值

Data ingestion

50 经验值

Data parity checks

50 经验值

Defining the schema

100 经验值

Performing data validation

100 经验值

50 经验值

Setting up the DAG

100 经验值

Creating the DAG

100 经验值

Defining pipeline execution order

100 经验值

Logs

50 经验值

Monitoring ETL logs

50 经验值

Monitoring forecasting logs

50 经验值

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

50 经验值

What could go wrong?

100 经验值

Monitoring vs Observability

100 经验值

Pipeline Monitoring

50 经验值

Categorizing API risks

100 经验值

Building a mitigation plan

50 经验值

Model Drift

50 经验值

Calculating a threshold and moving averages

100 经验值

Identifying model drift

100 经验值

Best practices

50 经验值

Development best practices

50 经验值

Scaling forecasting pipelines

50 经验值

Congratulations!

50 经验值

Designing Forecasting Pipelines for Production

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