H2O key concepts

In the following sections, we will identify and describe the key concepts of H2O that underlie the workflow steps of the previous section. These concepts are necessary to understand the rest of the book.

The data scientist's experience

The data scientist has a familiar experience in building H2O models at scale while being abstracted from the complexities of the infrastructure and architecture on the enterprise server cluster. This is further detailed in the following diagram:

Figure 2.2 – Details of the data scientist's experience with H2O Core

Data scientists use well-known unsupervised and supervised machine learning techniques that scale across the enterprise's distributed infrastructure and architecture. These techniques are written with the H2O model building API, which is written in familiar languages (such as Python, R, or Java) using familiar IDEs (for example, Jupyter or RStudio).

H2O Flow –...