H2O can get you to a similar end, though it is a much different tool — more similar to Scikit-learn, SparkML, or packages of choice from Github.
Monument is strictly no-code and everything is “batteries included” — a much lower hurdle for enterprise-grade analysis than firing up a code development environment. We enable automatic model selection with our Autopilot feature, and also have AutoML.
Ideal use cases for Monument are:
1. One-off analyses with quick turnaround, and
2. Rapid enterprise-deployment, basically anywhere between original data and BI/reporting.
Overall, H2O is oriented toward people who expect to write and maintain code, as in the chart below.
Many enterprises find that is “the high-interest credit card of technical debt” — the code is incredibly expensive to create and maintain, particularly alongside infrastructure. It generally leads to expensive HR and infrastructure commitments and maintenance.
Interested in learning more about Monument? Book a free introductory Zoom call here.
