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Implementing a Predictive Failure Warning System
In this hands-on lab, you will wire a predictive model into a GitHub Actions pipeline and run it with the `act` command. You will call a Python script during the workflow, track the dataset with DVC, and generate alerts and markdown reports when the predicted risk of failure is high. By the end of the lab, you will have a working predictive failure warning system that helps you move from reactive debugging to proactive prevention.
Lab Info
Table of Contents
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Challenge
Integrate a Predictive Model Into a CI/CD Pipeline
You will configure a GitHub Actions workflow in the provided repository to call a Python predictive model on each run, use its risk score as a conditional signal, and gate later jobs such as long-running tests or deployments.
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Challenge
Develop a System for Proactive Alerting and Reporting
You will turn raw model output into a clear markdown report, add a simple alerting step that triggers when the predicted failure risk is high, and surface that warning through build artifacts the team can review before promoting a change.
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Challenge
Optimize Model Reproducibility Within the Pipeline Using DVC
You will use a DVC YAML file to define reproducible stages for data and model artifacts, compare Git history for code with DVC history for data and models, and explain how this combination keeps predictions reliable over time in your CI CD pipeline.
Real skill practice before real-world application
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