Automated Retraining Pipelines with GitHub Actions
Learners will write a GitHub Actions workflow that triggers on a data schedule, runs training, evaluates the new model, and promotes it only when it beats the production baseline.
Why Automate Model Retraining?
Models trained on historical data degrade over time as real-world patterns shift. Manually retraining requires a data scientist to remember to do it, run scripts, evaluate results, and update deployment — a process prone to delays and human error. Automated retraining pipelines run on a schedule or when triggered by data arrival, automatically train a new model, evaluate it against the current champion, and promote it only if performance improves — with zero manual intervention.
GitHub Actions: Workflows and Triggers
GitHub Actions is a CI/CD platform built into GitHub. Workflows are defined in YAML files under .github/workflows/ and run in response to triggers: code pushes, pull requests, manual dispatches, or cron schedules. Each workflow consists of jobs (groups of steps) that run on hosted or self-hosted runners. For ML pipelines, a scheduled cron trigger on a cloud GPU runner is the most common pattern.
# .github/workflows/retrain.yml (YAML structure shown)
# name: Model Retraining Pipeline
#
# on:
# schedule:
# - cron: '0 2 * * 1' # every Monday at 2 AM UTC
# workflow_dispatch: # also allow manual trigger
#
# jobs:
# retrain:
# runs-on: ubuntu-latest
# steps:
# - uses: actions/checkout@v4
# - name: Set up Python
# uses: actions/setup-python@v5
# with:
# python-version: '3.11'
print('Workflow YAML structure shown above.')All lessons in this course
- Experiment Tracking with MLflow: Log Params, Metrics, and Artifacts
- Reproducible Environments with Docker for ML
- Model Registry: Staging, Production, and Archiving
- Automated Retraining Pipelines with GitHub Actions