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Automating Retraining with Apache Airflow

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Overview

This course introduces the orchestration of an automated retraining pipeline using Apache Airflow. Learners will design a workflow that integrates data processing, model training, and evaluation, ensuring that the ML model stays up-to-date. The course emphasizes real-world scheduling, error handling, and optimization of the automated tasks.

Syllabus

Unit 1: Introduction to Apache Airflow and DAGs

Transform a Function into a DAG
Turn Functions into Airflow Tasks
Controlling Workflow Timing in Airflow
Adding a Third Task to Your DAG
Measuring Your Workflow Output
Build a Time Formatting Workflow
Build a Three Step Greeting Workflow

Unit 2: Designing an ML Pipeline with Apache Airflow

Model Validation Logic in Airflow
Archiving Models in Your Pipeline
Adding Rollback to Your ML Pipeline
Build a Complete ML Pipeline DAG

Unit 3: Testing and Running ML Pipelines with Airflow CLI

Discover Your Airflow Pipelines
Inspecting Your ML Pipeline Structure
Test Your Full ML Pipeline
See All Tasks in Your Pipeline
Test an Individual Pipeline Task

Unit 4: Building an Automated ML Retraining Pipeline with Apache Airflow

Unpacking Data for ML Pipelines
Ensuring Data Flow in Model Training
Adding a Model Quality Gate
Build a Complete ML Retraining Pipeline

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