# Debugging & Troubleshooting

> Systematic ML debugging — data issues, model failures, performance profiling, and production diagnostics.

![Status: developing](https://img.shields.io/badge/status-developing-yellow)

Open [`17-debugging-troubleshooting.ipynb`](./17-debugging-troubleshooting.ipynb) for learning objectives, prerequisites, and recommended reading order.

## Topics Covered

- ML debugging workflow and failure modes
- Data quality issues: missing values, label errors, distribution shift
- Performance profiling: CPU, memory, GPU utilisation
- Gradient issues, overfitting/underfitting diagnosis
- Production monitoring and error analysis

## Prerequisites

[16-model-evaluation/README.md](../16-model-evaluation/README.md)

## What Comes Next

[18-low-code-ai-tools/README.md](../18-low-code-ai-tools/README.md)
