The AI Lifecycle with the H2O.ai Platform
About this course
The H2O.ai platform brings the full AI lifecycle into one coordinated environment : from raw data to production AI, across both predictive ML and generative AI. In 25 focused lessons, you'll see how enterprise teams use the platform end-to-end. You'll start with automated data prep, feature engineering, and AutoML in H2O Driverless AI, then move into explainability with SHAP and LIME, fairness testing with Disparate Impact Analysis, and full model lifecycle management in H2O MLOps — including deployment, A/B testing, autoscaling, and real-time drift monitoring. On the generative AI side, you'll learn how Enterprise h2oGPTe powers prompt engineering, multimodal RAG, LLM guardrails, and autonomous agent workflows. You'll also explore fine-tuning with LoRA and DPO in H2O Enterprise LLM Studio, and see how the H2O Super Agent and Agent Builder generate production-ready agents using CrewAI, LangGraph, and the OpenAI SDK. By the end, you'll understand how predictive ML and generative AI come together under one governance, security, and lifecycle model — and how to operationalize AI responsibly at enterprise scale.
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What you'll learn
- automated data preparation techniques
- feature engineering using H2O Driverless AI
- implementing explainability with SHAP and LIME
- conducting fairness testing with Disparate Impact Analysis
- model lifecycle management in H2O MLOps
- real-time drift monitoring
- fine-tuning generative models with LoRA and DPO
- creation of production-ready agents using CrewAI and OpenAI SDK
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