The workflow using H2O components

Now that we understand the roles and key features of H2O's machine learning at scale components, let's tie them together into a high-level workflow, as represented in the following diagram:

Figure 2.1 – A high-level machine learning at scale workflow with H2O

The workflow occurs in the following sequence:

1. The administrator configures H2O Enterprise Steam.
2. The data scientist logs into H2O Enterprise Steam and launches the H2O Core cluster (choosing either H2O-3 or H2O Sparkling Water).
3. The data scientist uses their favorite client to build models using the Python, R, or Java/Scala language flavor of the H2O model building API. The data scientist uses a UI or IDE to authenticate to H2O Enterprise Steam and connect to the H2O cluster that was started on H2O Enterprise Steam.
4. The data scientist uses the IDE to iterate through the model building steps with H2O.
5. After the data scientist decides...