Model generation 2016-02-03
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H2O AI

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Create, deploy and monitor ML models on a platform.
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H2O.ai is an advanced AI Cloud Platform designed to facilitate the development, operation, and innovation of AI in any environment. It provides a suite of tools to help create, deploy, and monitor AI models and applications.

Its flagship product, H2O Driverless AI, is an automated machine learning platform that democratizes AI, while H2O-3 is an open-source distributed machine learning platform.

H2O Document AI is a tool for extracting data with intelligence, H2O Hydrogen Torch is a no-code deep learning platform, and H2O Wave is a low-code AI app development framework.

H2O AI Feature Store helps to infuse data with intelligence, and H2O MLOps streamlines performance monitoring and rapid adaptation to changing conditions.

Finally, H2O AI AppStore provides industry and use case AI apps, and the H2O.AI Wiki is a source of up-to-date resources on AI and ML. H2O.ai's mission is to provide everyone access to AI technologies and empower them to make a positive impact.

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H2O AI was manually vetted by our editorial team and was first featured on November 13th 2022.
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Pros and Cons

Pros

Open-source distributed machine learning
Automated machine learning platform
Data extraction with intelligence
No-code deep learning platform
Feature store for intelligent data
Efficient performance monitoring
Rapid adaptation to changing conditions
Industry and use case apps
State-of-the-art cloud platform
Speedy and transparent operation
Model hosting and deployment
Model creation and deployment
Document data extraction
Cloud-based tool
Good customer support
User-friendly interface
Streamlined model operation
Innovation-friendly platform
Trusted by global organizations
Automated document processing
Good for various industries
Useful for fraud detection
Improves predictive maintenance
Aids in supply chain optimization
Effective for credit scoring
Easily accessible resources
Regular platform updates
Useful for strategic transformation
Robust security bulletins

Cons

Complex platform navigation
Oversaturation of features
Defaults to cloud use
Limited low-code options
Deep learning lacks code
Limited ML model support
Focus on democratization could limit advanced features
Resource-heavy platform
Monitors models, not development process
Fragmented platform suites

Q&A

What does H2O AI do?
How does H2O AI create, deploy and monitor ML models?
What is the H2O Driverless AI?
How does H2O-3 work as an open-source machine learning platform?
What functionalities does H2O Document AI provide?
Can you explain the no-code deep learning with H2O Hydrogen Torch?
What is the H2O Wave?
How does H2O AI Feature Store infuse data with intelligence?
How does H2O MLOps streamline performance monitoring?
What apps can I find in the H2O AI AppStore?
What resources can I find in the H2O.AI Wiki?
What is the mission of H2O.ai?
Can H2O.ai platform be used in any environment?
What industries can benefit from H2O.ai?
Who are some notable customers of H2O.ai?
How does H2O Driverless AI democratize AI?
What is the H2O AI Cloud?
How does H2O's AI Cloud platform ensure accuracy, speed and transparency?
How does H2O define its AI democratization goal?
What is the function of the H2O MLOps in the operating process?

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