What metrics would you use to evaluate H2O.ai's H2O Wave platform?

Introduction

Evaluating the success of H2O.ai's H2O Wave platform requires a comprehensive approach to product metrics. As an experienced product leader, I'll outline a structured framework that covers core metrics, supporting indicators, and risk factors while considering all key stakeholders involved in this low-code development platform.

Step 1

Product Context

H2O Wave is a low-code development platform that enables rapid creation of AI-powered applications. It's designed to bridge the gap between data scientists, developers, and business users, allowing for quick deployment of machine learning models into production environments.

Key stakeholders include:

1. Data scientists: Seeking to operationalize their models efficiently
2. Developers: Looking for tools to build AI-powered apps quickly
3. Business users: Needing access to AI capabilities without deep technical knowledge
4. IT departments: Concerned with security, scalability, and integration

User flow typically involves:

1. Model development: Data scientists create ML models using H2O.ai's tools
2. App creation: Developers or citizen developers use H2O Wave to build interfaces
3. Deployment: IT teams deploy the applications to production environments
4. Usage: Business users interact with the AI-powered applications

H2O Wave fits into H2O.ai's broader strategy of democratizing AI and making it accessible to a wider range of users. It complements their AutoML and MLOps offerings, creating a full-stack AI platform.

Compared to competitors like Dataiku or Databricks, H2O Wave focuses more on rapid application development and deployment, rather than just model creation or data processing.

In terms of product lifecycle, H2O Wave is in the growth stage. It has established a user base but is still expanding its features and market penetration.