Learn how to build and deploy AI solutions with H2O.ai’s suite of tools. This course introduces you to key platforms like Driverless AI, H2O Actions, Wave App, GenAI AppStore, LLM DataStudio, H2O LLMStudio, Enterprise GPTe, h2oGPT, and Eval Studio.
You will also gain hands-on experience preparing and visualizing data, automating machine learning workflows, deploying models, and applying generative AI capabilities for advanced tasks such as text generation and translation.
By the end, you’ll be able to navigate H2O.ai’s platforms and apply AI solutions across various industries and use cases.
What you'll learn
- Data Preparation and Visualization
Learn how to clean, transform, and explore data using H2O’s intuitive tools for efficient analysis.
- Automated Machine Learning with Driverless AI
Use Driverless AI to build, tune, and interpret machine learning models in a streamlined workflow.
- Model Deployment for Business Impact
Deploy AI models into production environments to drive actionable outcomes.
- Generative AI Applications
Leverage tools like h2oGPT and Enterprise GPTe for text generation, summarization, and language translation tasks.
Course Playlist on YouTube
H2O.ai Managed Cloud Overview | Build & Scale AI Across Your Enterprise
Explore H2O.ai’s Managed Cloud—your secure, enterprise-grade environment for building, deploying, and scaling AI and machine learning solutions. In...
Introduction to H2O.ai's Data Science and Machine Learning Platform
Want to learn how H2O.ai's platform simplifies data science workflows with seamless integration across features, notebooks, and AI engines like Dri...
Plan Projects, Explore , Visualize, Data Preparation
Discover how H2O's Driverless AI empowers data scientists with automated machine learning, data visualization, prep wizards, and live coding capabi...
Data Science and Machine Learning Techniques
Learn how to effortlessly upload datasets and leverage existing ones from the feature store for analysis. Follow along as we explore the intuitive ...
Model Consumption with H2O.ai Tools
Learn how to leverage H2O, an open-source library, to effortlessly create multi-page applications and chatbots using only Python. Explore the proce...
Training and Deploying Standalone LLM + Guardrails
Explore the process of training and deploying a standalone Large Language Model (LLM) with guardrails in this engaging demo.
Generative AI for Advanced RAG systems & AI Governance
In this demo, we navigate the H2O AI Cloud, where Enterprise GPTe, H2O GPT, and Eval Studio await exploration. Witness the creation of collections,...
How to Choose the Best AI Model for Business Success | Reduce Customer Churn by 20%
💡 Want to build AI models that maximize business value? This video explores how the H2O platform helps optimize models for real-world impact.
Using AI for Hypothesis Testing | Optimize Customer Churn with h2oGPTe
💡 Discover how to use h2oGPTe for collaborative, research-based hypothesis testing! In this video, we explore how AI can help predict and optimize...
Managing Projects and Feature Sets in Feature Store | Optimizing Data Access for AI
Learn how to efficiently manage projects and feature sets using Feature Store in H2O. This video explores how organizations can improve collaborati...
Understanding Data Lineage in H2O | Tracking Data for AI Models
Learn how data lineage in the H2O platform ensures transparency, traceability, and efficiency in AI model development. Feature Store and Driverless...
Data Profiling and Augmentation for AutoML | Improving AI Model Accuracy
Explore how data profiling and augmentation enhance AI model performance in AutoML. From data quality checks to feature engineering, learn how to o...
Uncovering Data Insights with H2O AutoInsights | AI-Powered Data Analysis
Learn how H2O AutoInsights automatically extracts valuable insights from your data using statistical and machine learning analysis. Gain a deeper u...
Causal AI and Uplift Modeling with H2O | Analyzing Treatment vs. Control Groups
Discover how H2O supports Causal AI with uplift modeling in Driverless AI and H2O3. Learn how to evaluate the impact of treatments versus control g...
Ensuring Fairness in AI with Disparate Impact Analysis | Bias Detection in Machine Learning
Learn how Disparate Impact Analysis helps assess fairness in Driverless AI models and detect potential bias in decision-making. This method ensures...
Harnessing Predictive & Generative AI with h2oGPTe | Automating Model Training & Deployment
Discover how h2oGPTe integrates predictive and generative AI to automate model training, deployment, and application building. See how AI agents st...
Optimizing AI Models with Predictive & Generative AI | Model Validation & Evaluation
Explore how predictive and generative AI enhance model validation, evaluation, and compliance within the H2O platform. Learn how to fine-tune AI pe...
Enhancing Machine Learning with H2O Feature Store | Efficient Feature Management
feature metadata, permissions, and access control to improve collaboration and model development.
Optimizing AI Model Experiments with Driverless AI | Parallel Experiment Tracking
Discover how Driverless AI streamlines model experiment tracking and resource optimization by running multiple AI models in parallel. Learn how aut...
Managing AI Model Experiments in Driverless AI | Collaboration & MLOps Integration
Learn how to store, share, and manage AI model experiments in Driverless AI for seamless collaboration and deployment. See how MLOps integration en...
Interpreting AI Models in Driverless AI | Fairness, Sensitivity & Model Transparency
Learn best practices for interpreting AI models in Driverless AI, ensuring transparency, fairness, and clear communication of limitations. Explore ...
Deploying AI Models with H2O MLOps | Scalable & Flexible Deployment Strategies
Learn how to deploy AI models efficiently using H2O MLOps, with support for A/B testing, Champion-Challenger setups, and scalable environments. Exp...
Managing the Full AI Model Lifecycle with H2O | Automation, Retraining & Deployment
Explore how H2O’s AI platform supports the full model lifecycle, from registration and deployment to automated retraining and deactivation. Learn h...
Monitoring AI Model Performance & Drift in Driverless AI | Real-Time Insights & Automation
Learn how Driverless AI enables real-time model monitoring, drift detection, and performance tracking for AI models in production. See how to analy...
Self-Identifying & Fixing AI Model Issues | Automated Error Handling in Driverless AI
Discover how Driverless AI enables AI models to self-identify and handle issues in real-time. Learn how monitoring, drift detection, and error hand...
