AI Agents Accelerate Data Science Workflows
- Agentic AI
- Enterprise h2oGPTe
- H2O Driverless AI
This lesson walks through how an AI agent inside Enterprise h2oGPTe speeds up the data science lifecycle, starting by connecting to a dataset in S3 and running autonomous profiling and correlation analysis. It covers the agent generating visual analytics like distribution plots and correlation heat maps, then synthesizing findings into a business focused narrative rather than just showing charts. It shows the agent configuring and monitoring a modeling experiment through H2O Driverless AI, then pulling SHAP values to explain feature importance. It closes by noting the agent is embedded directly within the ML platform, not a separate tool.
Key takeaways
- How an AI agent autonomously profiles a dataset pulled from S3, including correlation and distribution analysis
- What makes the agent's output different, synthesizing charts into a business focused narrative, not just visuals
- How the agent configures and monitors a modeling experiment directly through H2O Driverless AI
- Why pulling SHAP values through the agent helps explain which features drive a model's predictions
- The agent is embedded directly within the ML platform rather than operating as a separate standalone tool
Start owning your AI