Hydrogen Torch: Recap of Your First Model
- Predictive AI
- H2O Hydrogen Torch
- H2O MLOps
This lesson recaps the first modules of the Hydrogen Torch Starter Course, from importing the coins image regression dataset through building a model with default, Kaggle grandmaster derived hyperparameters. It reviews how validation MAE measures performance, and how the best and worst validation samples showed that low lighting images produced higher loss. It revisits the two use case examples, exact ATM coin counting versus rapid approximate counting, to show how acceptable performance depends on the task. It previews deploying the finished model through the Hydrogen Torch UI, a Python scoring pipeline, or an H2O MLOps pipeline.
Key takeaways
- How a lower validation MAE score indicates predictions closer to the true coin count
- What the best and worst validation samples revealed about the impact of image lighting on accuracy
- Why the same model can be unsuitable for exact ATM counting yet acceptable for rapid approximation
- The next module covers improving the model further using grid search on its hyperparameters
- How a finished model can deploy through the Hydrogen Torch UI, a Python pipeline, or an H2O MLOps pipeline
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