Production Tutorials¶
Learn how to deploy PanelBox models in production environments. These tutorials cover prediction workflows, model serialization, production pipelines, validation strategies, model versioning, and a complete case study on bank Loss Given Default (LGD) modeling.
Prerequisites
Understanding of PanelBox model fitting and results interpretation. Familiarity with Python packaging and deployment concepts is helpful for advanced tutorials.
Notebooks¶
| # | Tutorial | Level | Time | Colab |
|---|---|---|---|---|
| 1 | Prediction Fundamentals | Intermediate | ~20 min |  |
| 2 | Save & Load Models | Intermediate | ~20 min | |
| 3 | Production Pipelines | Advanced | ~35 min | |
| 4 | Model Validation | Advanced | ~30 min | |
| 5 | Model Versioning | Advanced | ~30 min | |
| 6 | Case Study: Bank LGD | Advanced | ~45 min | |
Solutions¶
Solutions with complete code and explanations are available for all tutorials:
| Tutorial | Solution |
|---|---|
| Prediction Fundamentals | |
| Save & Load Models | |
| Production Pipelines | |
| Model Validation | |
| Model Versioning | |
| Case Study: Bank LGD | |
What You Will Learn¶
Getting Started (Tutorials 1--2): Generate predictions from fitted models (in-sample and out-of-sample), and serialize/deserialize models for reuse without re-estimation.
Advanced Deployment (Tutorials 3--5): Build end-to-end production pipelines with data validation, implement model validation frameworks for monitoring drift, and manage model versions across iterations.
Case Study (Tutorial 6): Apply everything in a real-world bank LGD (Loss Given Default) modeling scenario, from data preparation through model deployment and monitoring.