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· 14 min read
Sergio Perez

Overview​

This tutorial blog highlights how H2O AI Cloud (HAIC) lets novice and expert data scientists build state-of-the-art machine learning (ML) models by supporting crucial parts of the machine learning life cycle. In particular, this tutorial utilizes H2O Hydrogen Torch and other HAIC applications to build, deploy, and score a state-of-the-art image classification model. This model is capable of determining whether an image depicts a car or coffee (or a cup of coffee). The following applications are utilized:

The logos of H2O Label Genie, H2O Hydrogen Torch, and H2O MLOps

ApplicationHow this tutorial uses the applicationDetails of the application
H2O Label Genie (v0.3.0)To label (prepare) the data for the image classification model.H2O Label Genie expedites the process of labeling data by utilizing zero-shot learning models.
H2O Hydrogen Torch (v1.3.0)To build the image classification model.
  • Labeled data in H2O Label Genie can be downloaded and imported to H2O Hydrogen Torch.
  • H2O Hydrogen Torch is an application that lets novice and expert data scientists build deep learning models for diverse problem types in computer vision, natural language, and audio. No code is required.
  • H2O Hydrogen Torch lets you generate good models with default hyperparameter values derived from best model training practices used by top Kaggle grandmasters.
H2O MLOps (v0.60.1)To deploy the image classification model.Built H2O Hydrogen Torch models can be deployed to H2O MLOps right from the H2O Hydrogen Torch UI.