Learn how to use H2O Cloud AI Developer Services to build, automate, and share AI solutions for your business needs. This course covers key services across tabular data, language tasks, computer vision, and developer tools.

You’ll explore practical workflows, including AutoML for tabular data, NLP with Hydrogen Torch, data labeling with Label Genie, and using AI assistants for coding tasks.

By the end, you’ll know how to apply H2O Cloud AI Developer Services to real-world AI projects across different domains.

 

What you'll learn

  • Tabular AI Services
    Learn to use AutoML for tabular data, handle large datasets, and connect to diverse data sources.
  • Language AI Services
    Build and deploy NLP solutions using tools like Hydrogen Torch and customize models for enterprise use cases.
  • Vision AI Services
    Label data with Label Genie, work with document AI, and develop computer vision models.
  • AI Assistants for Developers
    Boost productivity with AI code suggestions and automations designed for developers.

Course access

H2O.ai Certificate H2O.ai Certificate
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Audrey Létévé, Principal Customer Data Scientist

  • Principal Data Scientist at H2O.ai, specializing in leading complex Machine Learning projects from ideation to production, with a keen interest in Model Ops and a strong background in statistics.

  • Her expertise covers a broad range of industries such as insurance, energy, and services, enabling her to communicate effectively with both technical and non-technical stakeholders.

  • Holding a Master of Science in Mathematics and Statistics from Université Aix-Marseille II, Audrey has a proven track record of enhancing business strategies and objectives through data analysis and model development across various data science roles.

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Jon Farland, Director of Solutions Eng.

Jon Farland is the Director of the H2O.ai Solutions Engineering team. He has spent the better part of the last decade building analytical solutions at the intersection of technology, finance and energy. He has used H2O extensively to develop high performing models, communicate findings across stakeholders and to lead ROI growth from data science initiatives.