Michelle Tanco is Head of Product at H2O.ai, spanning the company's Predictive AI and Agentic AI products. She connects the technical and business sides of each product, working with engineering, solution engineering, marketing and go-to-market teams on how H2O's capabilities reach customers. She leads many of H2O's customer demos, trainings and partner sessions, explaining complex AI in plain terms.Before H2O.ai, Michelle was a senior data science consultant at Teradata, working on analytics projects for businesses across industries. She holds a BA in mathematics and computer science from Ursinus College.
The AI Lifecycle with the H2O.ai Platform: From Data to Production
- Predictive AI
- Observability
- Agentic AI
- Generative AI
- H2O AI Managed Cloud
- H2O Driverless AI
- Last updated 03/2026
- English
- Intermediate
- 48m · 25 lessons
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About this course
This course provides a complete walkthrough of the AI lifecycle using the H2O.ai platform.
Designed for data science and AI professionals, it explains how to build, deploy, and manage both predictive machine learning models and generative AI applications within a secure enterprise environment.
You will see how the platform connects each stage of the data science workflow. The course covers everything from raw data preparation and automated feature engineering to model deployment, real-time monitoring, and strict regulatory compliance.
You will also learn how to integrate modern AI agents and Large Language Models (LLMs) into standard enterprise workflows.
What you'll learn
Skills you will take away
Data Preparation & Feature Engineering
Learn how to automate data transformations, generate synthetic data, and manage scalable offline and online feature stores.
Predictive Model Development
Understand how to train, track, and optimize machine learning models while ensuring explainability, bias testing, and alignment with business ROI.
Enterprise MLOps & Monitoring
See how to deploy models, manage artifact registries, optimize compute resources, and monitor production models for real-time data drift.
Generative AI & LLM Management
Learn to test prompts, apply instruction tuning (DPO), set up security guardrails, and evaluate LLM responses using LLM-as-a-judge metrics.
RAG & Agentic Workflows
Understand how to build multimodal RAG pipelines and orchestrate AI agents to automate data science tasks and multi-step workflows.
AI Governance & Compliance
Learn how to enforce role-based access controls, maintain data traceability, and automatically generate model documentation and audit trails.
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Learn how enterprise teams move from data profiling and feature engineering through automated machine learning, explainability, and production deployment — all within a single, coordinated platform. Rather than stitching together disconnected tools, H2O.ai is designed to support the full journey from experimentation to operations with shared governance, security controls, and lifecycle management built in. The course also covers how traditional predictive modeling connects with modern generative AI capabilities and agent-driven workflows. What You Will Learn : ✦ Data Preparation & Feature Engineering: Automated profiling, transformations, and feature pipelines from raw data to model-ready inputs. ✦ Automated Machine Learning: Model training, explainability, and bias testing with H2O Driverless AI. ✦ MLOps & Production Deployment: Model registration, real-time serving, drift monitoring, and lifecycle management. ✦ Generative AI & Agentic Workflows: Connecting predictive models with LLMs and autonomous agents via Enterprise h2oGPTe. ✦ Enterprise Governance: Unified security controls, audit logging, and compliance across the entire AI lifecycle. 🔗 H2O.ai Platform Overview: https://h2o.ai/ 🎓 H2O.ai University: https://h2o.ai/university/ 📚 H2O.ai Documentation: https://docs.h2o.ai/
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