Finishing the Hydrogen Torch Starter Course
- Predictive AI
- H2O Hydrogen Torch
- H2O MLOps
This short lesson closes out the Hydrogen Torch Starter Course, recapping that default hyperparameter values drawn from top Kaggle grandmasters let you generate strong deep learning models without extensive coding. It points learners with an Adobe LMS account to the certification quiz for a completion certificate, and for anyone without LMS access, to certification@h2o.ai instead. It previews the next course, Hydrogen Torch Model Deployment, which covers three ways to deploy a trained model: through the Hydrogen Torch UI, through an H2O MLOps pipeline, or through a Python scoring pipeline.
Key takeaways
- How default hyperparameter values based on Kaggle grandmaster practices reduce the coding needed for strong models
- What to do to take the certification quiz, or who to contact if you don't have Adobe LMS access
- What the three deployment methods are covered in the follow-up Hydrogen Torch Model Deployment course
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