Benchmark Abstract

Using the H2O distributed database project, we are testing two machine learning algorithms, GLM (Generalized Linear Model) and DL (Deep Learning), in an on-premises virtualized environment and the Bigstep Metal Cloud. The benchmark analyzes the performance of both platforms for parsing the dataset and building the two models.

What's Inside

1. Execution Times
   Comparative times for data parsing and building the algorithm models.
2. Dataset Size Variations
   Performance gaps differences when the workload size changes.
3. Results Analysis
   insights into the factors that are influencing the results.