Menu
H2O.ai
- Last updated: 13 Aug 2024
What is H2O.ai?
H2O.ai is an AI company providing an end-to-end platform for predictive and generative AI, suitable for on-premise, cloud, and air-gapped VPC deployments. The platform supports a wide range of AI-driven tools such as H2O AI Cloud, h2oGPT, and H2O Driverless AI, offering solutions in document and data processing, automated machine learning, and deep learning. H2O.ai aims to democratize AI through its comprehensive suite of products that include functionalities like model evaluation, data extraction, and large language model fine-tuning, all designed to enhance business processes and decision-making.
H2O.ai use cases
- A financial analyst using H2O Driverless AI for credit scoring and fraud detection.
- A healthcare provider utilizing H2O Document AI to automate medical document workflows.
- A marketing team employing H2O Wave to build interactive AI dashboards to analyze customer sentiment.
- A data scientist fine-tuning large language models with H2O LLM Studio for specific enterprise applications.
- A manufacturing firm leveraging H2O Hydrogen Torch for defect detection in production lines.
H2O.ai functionalities
- Automated Machine Learning: Democratizes AI with AutoML.
- Document AI Extraction: Extracts data with intelligence.
- Low-Code App Development: Builds AI applications quickly.
- Open-Source Models: Supports scalable machine learning.
H2O.ai review summary
Pros
H2O Driverless AI significantly enhances productivity and simplifies machine learning processes. Users appreciate its excellent support, rapid iteration, top-quality performance, comprehensive automation, and intuitive UI. Many find it to be a superior tool compared to alternatives, praising its ability to streamline ML operations, integrate well with existing workflows, and offer valuable resources for the community.
Cons
While H2O Driverless AI excels in many areas, some users find the pricing high, especially for smaller organizations. Other concerns include the complexity of debugging, limited deployment support for edge computing, and some missing data manipulation features. The documentation and integration with other tools could also use improvement.
Excellent Support and Rapid Iteration
The support team is responsive and enhancements are quickly implemented.
High Cost
The pricing is considered high, especially for smaller businesses or early adopters.
When we report an error or make a suggestion for enhancement, a new release is out within weeks. Excellent support for commercial product Driverless AI. Rapid iteration.
Price is high for closed source product, Driverless AI. The license fee and the lack of pay per use pricing models are a hurdle in any grassroots initiative.
Top-Notch Performance and Automation
Driverless AI automates complex ML tasks effectively and outperforms other major tools.
Limited Debugging Support
Debugging messages can be cryptic and not always user-friendly.
The performance of DAI is far beyond what can be achieved with tools from Amazon, Google, and Microsoft. Driverless AI has strong capability on the auto feature engineering and system visualization.
Somewhat cryptic debugging msgs in H2O-3. One downside of H2O.ai is its bugs which do not return human-readable debugging statements.
Ease of Use and Intuitive UI
The platform is user-friendly with good UI design and easy data import and visualization.
Documentation Challenges
Several users feel the documentation could be clearer and more comprehensive.
Easy to use with good UI design and automated ML function. The web front end known as flow is really easy to use.
Documentation in general can be improved. Better instructions would be helpful, as would clearer tutorials.
High Efficiency and Productivity
The tool significantly reduces model development time and enhances productivity in ML tasks.
Limited Data Manipulation Features
H2O Frames have limited data processing options compared to other tools like pandas or pyspark.
We can develop a model in a fraction of the time it would take us using the traditional modelling workflow. It allows you to test various models before you decide which you want to fine-tune.
H2O Frames have very limited data processing options compared to python pandas or pyspark dataframes.
Comprehensive Automation
Driverless AI offers end-to-end automation, from feature selection to model deployment.
Limited Support for Edge Computing
Current deployment support does not fully meet the needs of IoT and edge computing environments.
H2O offers a well-validated, fully automated, rigorous machine learning pipeline including state of the art model interpretation. It's a great tool for really quick prototyping.
It is great if Driverless AI could support deployment for edge computing, which is common in IoT world.
Pros
H2O Driverless AI significantly enhances productivity and simplifies machine learning processes. Users appreciate its excellent support, rapid iteration, top-quality performance, comprehensive automation, and intuitive UI. Many find it to be a superior tool compared to alternatives, praising its ability to streamline ML operations, integrate well with existing workflows, and offer valuable resources for the community.
Excellent Support and Rapid Iteration
The support team is responsive and enhancements are quickly implemented.
When we report an error or make a suggestion for enhancement, a new release is out within weeks. Excellent support for commercial product Driverless AI. Rapid iteration.
Top-Notch Performance and Automation
Driverless AI automates complex ML tasks effectively and outperforms other major tools.
The performance of DAI is far beyond what can be achieved with tools from Amazon, Google, and Microsoft. Driverless AI has strong capability on the auto feature engineering and system visualization.
Ease of Use and Intuitive UI
The platform is user-friendly with good UI design and easy data import and visualization.
Easy to use with good UI design and automated ML function. The web front end known as flow is really easy to use.
High Efficiency and Productivity
The tool significantly reduces model development time and enhances productivity in ML tasks.
We can develop a model in a fraction of the time it would take us using the traditional modelling workflow. It allows you to test various models before you decide which you want to fine-tune.
Comprehensive Automation
Driverless AI offers end-to-end automation, from feature selection to model deployment.
H2O offers a well-validated, fully automated, rigorous machine learning pipeline including state of the art model interpretation. It's a great tool for really quick prototyping.
Cons
While H2O Driverless AI excels in many areas, some users find the pricing high, especially for smaller organizations. Other concerns include the complexity of debugging, limited deployment support for edge computing, and some missing data manipulation features. The documentation and integration with other tools could also use improvement.
High Cost
The pricing is considered high, especially for smaller businesses or early adopters.
Price is high for closed source product, Driverless AI. The license fee and the lack of pay per use pricing models are a hurdle in any grassroots initiative.
Limited Debugging Support
Debugging messages can be cryptic and not always user-friendly.
Somewhat cryptic debugging msgs in H2O-3. One downside of H2O.ai is its bugs which do not return human-readable debugging statements.
Documentation Challenges
Several users feel the documentation could be clearer and more comprehensive.
Documentation in general can be improved. Better instructions would be helpful, as would clearer tutorials.
Limited Data Manipulation Features
H2O Frames have limited data processing options compared to other tools like pandas or pyspark.
H2O Frames have very limited data processing options compared to python pandas or pyspark dataframes.
Limited Support for Edge Computing
Current deployment support does not fully meet the needs of IoT and edge computing environments.
It is great if Driverless AI could support deployment for edge computing, which is common in IoT world.
H2O.ai user reviews
Excellent Product
Pros:
Helps speed up time to production and simplifies the entire ML process.
Cons:
Nothing. The team has been great and the product wo...
Helps speed up time to production and simplifies the entire ML process.
Cons:
Nothing. The team has been great and the product wo...
Pros:
Helps speed up time to production and simplifies the entire ML process.
Cons:
Nothing. The team has been great and the product works very well.
Helps speed up time to production and simplifies the entire ML process.
Cons:
Nothing. The team has been great and the product works very well.
Read more
Read less
Exceptional Machine Learning Tool
Overall:
DAI assists greatly in the automation of our services. It greatly streamlines the process of feature selection, generation, model constr...
DAI assists greatly in the automation of our services. It greatly streamlines the process of feature selection, generation, model constr...
Overall:
DAI assists greatly in the automation of our services. It greatly streamlines the process of feature selection, generation, model construction, model testing, and ensemble building and deployment.
Pros:
The performance of DAI is far beyond what can be achieved with tools from Amazon, Google, and Microsoft. A great deal of the human expertise of Kaggle Grandmasters has been incorporated into the product. beyond overall performance, a key advantage of the product is very rapid product development and iteration. When we report an error or make a suggestion for enhancement, a new release is out within weeks.
Cons:
Nothing. The product works extremely well.
Reasons for Choosing H2O Driverless AI:
Best overall performance, best support, cost effective.
DAI assists greatly in the automation of our services. It greatly streamlines the process of feature selection, generation, model construction, model testing, and ensemble building and deployment.
Pros:
The performance of DAI is far beyond what can be achieved with tools from Amazon, Google, and Microsoft. A great deal of the human expertise of Kaggle Grandmasters has been incorporated into the product. beyond overall performance, a key advantage of the product is very rapid product development and iteration. When we report an error or make a suggestion for enhancement, a new release is out within weeks.
Cons:
Nothing. The product works extremely well.
Reasons for Choosing H2O Driverless AI:
Best overall performance, best support, cost effective.
Read more
Read less
Excellent framework and application
What do you like best about H2O?
Excellent support for commercial product Driverless AI. Rapid iteration. Performance is generally better than on...
Excellent support for commercial product Driverless AI. Rapid iteration. Performance is generally better than on...
What do you like best about H2O?
Excellent support for commercial product Driverless AI. Rapid iteration. Performance is generally better than one can be achieved in code. Review collected by and hosted on G2.com.
What do you dislike about H2O?
Actually nothing. The combination of proprietary and open source tools, Driverless AI and H2O, provide tools across a full range of use cases. Review collected by and hosted on G2.com.
Recommendations to others considering H2O:
Take advantage of the 30 day trial. Review collected by and hosted on G2.com.
What problems is H2O solving and how is that benefiting you?
We work in both financial services and biological research. Review collected by and hosted on G2.com.
Excellent support for commercial product Driverless AI. Rapid iteration. Performance is generally better than one can be achieved in code. Review collected by and hosted on G2.com.
What do you dislike about H2O?
Actually nothing. The combination of proprietary and open source tools, Driverless AI and H2O, provide tools across a full range of use cases. Review collected by and hosted on G2.com.
Recommendations to others considering H2O:
Take advantage of the 30 day trial. Review collected by and hosted on G2.com.
What problems is H2O solving and how is that benefiting you?
We work in both financial services and biological research. Review collected by and hosted on G2.com.
Read more
Read less
Workflows for quick ML prototyping
What do you like best about H2O?
They developed top-quality open source tools, including the H2O-3 and AutoML families. I do not have a license f...
They developed top-quality open source tools, including the H2O-3 and AutoML families. I do not have a license f...
What do you like best about H2O?
They developed top-quality open source tools, including the H2O-3 and AutoML families. I do not have a license for their Driverless AI, but my experience with it through tutorials and other demos has been superb. I should mention that their efforts to develop frameworks for ML interpretability are spot on, and their learning center is shaping up as a valuable resource to the community in general. The interfaces with R and Python enable a smooth transition of pre-existing workflows into the H2O framework. Review collected by and hosted on G2.com.
What do you dislike about H2O?
Somewhat cryptic debugging msgs in H2O-3. They support specific packages for manipulating data (data.table in R, datataable in Python) for the sake of speed and big data maneuverability, although many users may find this limiting. Driverless AI may not be affordable to the small fish in the pond. Review collected by and hosted on G2.com.
Recommendations to others considering H2O:
One should leverage all the resources available to test their products before buying. Review collected by and hosted on G2.com.
What problems is H2O solving and how is that benefiting you?
I have mostly used their AutoML to build quick ML/AI prototype solutions in different domains. Review collected by and hosted on G2.com.
They developed top-quality open source tools, including the H2O-3 and AutoML families. I do not have a license for their Driverless AI, but my experience with it through tutorials and other demos has been superb. I should mention that their efforts to develop frameworks for ML interpretability are spot on, and their learning center is shaping up as a valuable resource to the community in general. The interfaces with R and Python enable a smooth transition of pre-existing workflows into the H2O framework. Review collected by and hosted on G2.com.
What do you dislike about H2O?
Somewhat cryptic debugging msgs in H2O-3. They support specific packages for manipulating data (data.table in R, datataable in Python) for the sake of speed and big data maneuverability, although many users may find this limiting. Driverless AI may not be affordable to the small fish in the pond. Review collected by and hosted on G2.com.
Recommendations to others considering H2O:
One should leverage all the resources available to test their products before buying. Review collected by and hosted on G2.com.
What problems is H2O solving and how is that benefiting you?
I have mostly used their AutoML to build quick ML/AI prototype solutions in different domains. Review collected by and hosted on G2.com.
Read more
Read less
Driverless AI application for Auoma...
What do you like best about H2O?
Easy to use with good UI design and automated ML function. Driverless AI has strong capability on the auto featu...
Easy to use with good UI design and automated ML function. Driverless AI has strong capability on the auto featu...
What do you like best about H2O?
Easy to use with good UI design and automated ML function. Driverless AI has strong capability on the auto feature engineering and system visualization. The auto feature engineering has supported different machine learning algorithm (Random Forest, Decision Tree, Neural Network, Deep Learning, etc.) and feature parameter tuning (accuracy, time, system computing etc.) The system also helps user to reduce time and efforts for hyparemeter tuning and compare the model with different settings. This will optimize the process and provide the most efficient model for prediction in classification or regression domain. Besides, Driverless AI also has good UI design and visualization. The UI also supports end user to quickly import data, visualize data in different categories, as well as check on the system running and performance during the Auto ML process. The end user could also observe experiment summary and accuracy matrix, as well as model comparison in term of accuracy.
In addition, Driverless AI also supports the AI Interpretation to explain on the model and performance. This function is very helpful to end user for understanding the Machine Learning blackbox, as well as management team for decision making based on extensive information. Review collected by and hosted on G2.com.
What do you dislike about H2O?
It is great if Driverless AI could support deployment for edge computing, which is common in IoT world. The edge computing will require efficient computing and good accuracy with AutoML algorithm. This will help much the customer for deployment. Review collected by and hosted on G2.com.
Recommendations to others considering H2O:
Yes, H2O.ai DriverlessAI is a good AutoML platform for company, which does not have Data Scientist. Review collected by and hosted on G2.com.
What problems is H2O solving and how is that benefiting you?
Data analytics for predictive maintenance. It could make easy-to-use for system operator. Review collected by and hosted on G2.com.
Easy to use with good UI design and automated ML function. Driverless AI has strong capability on the auto feature engineering and system visualization. The auto feature engineering has supported different machine learning algorithm (Random Forest, Decision Tree, Neural Network, Deep Learning, etc.) and feature parameter tuning (accuracy, time, system computing etc.) The system also helps user to reduce time and efforts for hyparemeter tuning and compare the model with different settings. This will optimize the process and provide the most efficient model for prediction in classification or regression domain. Besides, Driverless AI also has good UI design and visualization. The UI also supports end user to quickly import data, visualize data in different categories, as well as check on the system running and performance during the Auto ML process. The end user could also observe experiment summary and accuracy matrix, as well as model comparison in term of accuracy.
In addition, Driverless AI also supports the AI Interpretation to explain on the model and performance. This function is very helpful to end user for understanding the Machine Learning blackbox, as well as management team for decision making based on extensive information. Review collected by and hosted on G2.com.
What do you dislike about H2O?
It is great if Driverless AI could support deployment for edge computing, which is common in IoT world. The edge computing will require efficient computing and good accuracy with AutoML algorithm. This will help much the customer for deployment. Review collected by and hosted on G2.com.
Recommendations to others considering H2O:
Yes, H2O.ai DriverlessAI is a good AutoML platform for company, which does not have Data Scientist. Review collected by and hosted on G2.com.
What problems is H2O solving and how is that benefiting you?
Data analytics for predictive maintenance. It could make easy-to-use for system operator. Review collected by and hosted on G2.com.
Read more
Read less
h2o is my personal data scientist
What do you like best about H2O?
h2o offers a well validated, fully automated, rigorous machine learning pipeline including state of the art mode...
h2o offers a well validated, fully automated, rigorous machine learning pipeline including state of the art mode...
What do you like best about H2O?
h2o offers a well validated, fully automated, rigorous machine learning pipeline including state of the art model interpretation allowing for prediction and inferences. Review collected by and hosted on G2.com.
What do you dislike about H2O?
i have nothing dislike about h2o’s products. Review collected by and hosted on G2.com.
What problems is H2O solving and how is that benefiting you?
my scientific questions involve biomedicine, neuroscience, and psychology Review collected by and hosted on G2.com.
h2o offers a well validated, fully automated, rigorous machine learning pipeline including state of the art model interpretation allowing for prediction and inferences. Review collected by and hosted on G2.com.
What do you dislike about H2O?
i have nothing dislike about h2o’s products. Review collected by and hosted on G2.com.
What problems is H2O solving and how is that benefiting you?
my scientific questions involve biomedicine, neuroscience, and psychology Review collected by and hosted on G2.com.
Read more
Read less
Excellent machine learning tools
What do you like best about H2O?
Good integration between development and production. Simple and powerful visual interface to complex backend. Ve...
Good integration between development and production. Simple and powerful visual interface to complex backend. Ve...
What do you like best about H2O?
Good integration between development and production. Simple and powerful visual interface to complex backend. Very rapid iteration of products, continuous improvement. Review collected by and hosted on G2.com.
What do you dislike about H2O?
Price is high for closed source product, Driverless AI. It’s worth it if you have a serious application, but the cost is an impediment to early adoption. Review collected by and hosted on G2.com.
What problems is H2O solving and how is that benefiting you?
Streamlining our process for generating and deploying machine learning models. Review collected by and hosted on G2.com.
Good integration between development and production. Simple and powerful visual interface to complex backend. Very rapid iteration of products, continuous improvement. Review collected by and hosted on G2.com.
What do you dislike about H2O?
Price is high for closed source product, Driverless AI. It’s worth it if you have a serious application, but the cost is an impediment to early adoption. Review collected by and hosted on G2.com.
What problems is H2O solving and how is that benefiting you?
Streamlining our process for generating and deploying machine learning models. Review collected by and hosted on G2.com.
Read more
Read less
Great product
Pros:
Simplified model build and tuning, easy to use, generated doc for experiment that summarizes what was done
Cons:
Wish the price c...
Simplified model build and tuning, easy to use, generated doc for experiment that summarizes what was done
Cons:
Wish the price c...
Pros:
Simplified model build and tuning, easy to use, generated doc for experiment that summarizes what was done
Cons:
Wish the price could be more affordable so more people could purchase and use
Reasons for Choosing H2O Driverless AI:
Easy to use and great model build
Simplified model build and tuning, easy to use, generated doc for experiment that summarizes what was done
Cons:
Wish the price could be more affordable so more people could purchase and use
Reasons for Choosing H2O Driverless AI:
Easy to use and great model build
Read more
Read less
A very good productivity tool for d...
Overall:
From biomarkers, clinical data, HR, finance and manufacturing, any data owner can run native experiments. More advanced users can use th...
From biomarkers, clinical data, HR, finance and manufacturing, any data owner can run native experiments. More advanced users can use th...
Overall:
From biomarkers, clinical data, HR, finance and manufacturing, any data owner can run native experiments. More advanced users can use the software to increase productivity and prune hypotheses quicky.
Pros:
Drag and drop a dataset, click a button, presto! You have a model training …
Cons:
The license fee and the lack of pay per use pricing models are a hurdle in any grassroots initiative.
Reasons for Choosing H2O Driverless AI:
Amazing service and customer support. Easy to use and install.
From biomarkers, clinical data, HR, finance and manufacturing, any data owner can run native experiments. More advanced users can use the software to increase productivity and prune hypotheses quicky.
Pros:
Drag and drop a dataset, click a button, presto! You have a model training …
Cons:
The license fee and the lack of pay per use pricing models are a hurdle in any grassroots initiative.
Reasons for Choosing H2O Driverless AI:
Amazing service and customer support. Easy to use and install.
Read more
Read less
Make more with less time
Pros:
We can develop a model in a fraction of the time it would take us using the traditional modelling workflow.
Cons:
Some recent fea...
We can develop a model in a fraction of the time it would take us using the traditional modelling workflow.
Cons:
Some recent fea...
Pros:
We can develop a model in a fraction of the time it would take us using the traditional modelling workflow.
Cons:
Some recent features such as Bring Your Own Recipe are great but still need lots of development to be relevant to some fields.
Alternatives Considered:
DataRobot
We can develop a model in a fraction of the time it would take us using the traditional modelling workflow.
Cons:
Some recent features such as Bring Your Own Recipe are great but still need lots of development to be relevant to some fields.
Alternatives Considered:
DataRobot
Read more
Read less
Overall
H2O Driverless AI stands out for its excellent support, user-friendly interface, top-notch performance, and comprehensive automation, making it highly effective for streamlining ML processes. These strengths enable rapid model development and efficient workflows. However, the high cost can deter smaller organizations, and some challenges exist with cryptic debugging messages, limited data processing features, and documentation that could be improved. If you seek an efficient, high-performing AutoML tool and can justify the investment, H2O Driverless AI is a powerful and valuable choice. The overall sentiment around the tool is largely positive, with substantial praise for its capabilities and support.
H2O.ai pricing
Below is the pricing information extracted from the provided data for H2O.ai.
| Feature | Enterprise h2oGPTe | H2O LLM Studio |
|---|---|---|
| Monthly Price | Contact Sales | Contact Sales |
| Document Search | Included | Included |
| On Premise | Included | Included |
| Cloud VPC | Included | Included |
| Customization Options | Yes | Yes |
| Guardrails | Yes | Yes |
User reviews
There are no reviews yet. Be the first one to write one.
You must be logged in to submit a review.
H2O.ai FAQ
What is H2O.ai?
H2O.ai is a comprehensive AI platform that includes services for predictive and generative AI. It offers tools for building, deploying, and managing AI applications across various sectors like financial services, healthcare, and more.
What is the H2O Driverless AI?
H2O Driverless AI is an AutoML platform that automates machine learning workflows, feature engineering, model validation, hyperparameter tuning, and more, making it easier for users without deep data science expertise to build AI models.
What are the deployment options for H2O.ai?
H2O.ai supports various deployment options including air-gapped, on-premises, private cloud, and managed cloud environments, ensuring flexibility and control over data and prompts.
What is h2oGPT?
h2oGPT is a tool for creating private, offline chatbot applications using open-source H2O LLM Studio Platform. It supports the integration of various large language models (LLMs) and embedding models.
What is H2O AI Cloud?
H2O AI Cloud is a state-of-the-art AI platform that combines predictive and generative AI tools to develop, deploy, and manage AI applications effectively. It supports various AI models and offers scalability with Kubernetes.
What is H2O Document AI?
H2O Document AI uses machine learning techniques like intelligent character recognition and natural language processing to extract data from documents, making data handling more efficient and accurate.
What are the features of H2O Eval Studio?
H2O Eval Studio is a tool for assessing the performance, reliability, and safety of LLM-based applications. It features customizable evaluation parameters, executive dashboards, and advanced insights for model monitoring.
What is H2O Wave?
H2O Wave is an open-source Python development framework used for building real-time interactive AI applications with sophisticated visualizations. It simplifies the development process with user-interface components and chart templates.
What types of AI models does H2O.ai support?
H2O.ai supports a wide range of AI models, including proprietary LLMs like Gemini, Claude, GPT-3.5 and 4, as well as open-source models like H2O Danube, Llama3, and more. It allows users to choose models based on their requirements.
How does H2O Hydrogen Torch help in deep learning?
H2O Hydrogen Torch is a no-code deep learning tool designed to make state-of-the-art model training accessible. It supports various data types and deep learning tasks, providing a streamlined interface for model development and deployment.
H2O.ai alternatives & similar tools
1
4.7 (133)
Writerly utilizes AI to enhance productivity for businesses and creators, enabling no-code content creation, editing, and publishing.
1
4.7 (133)
Writerly utilizes AI to enhance productivity for businesses and creators, enabling no-code content creation, editing, and publishing.
Writerly utilizes AI to enhance productivity for businesses and creators, enabling no-code content creation, editing, and publishing.
About the author
M. Kooiker
As an experienced online marketeer with a longstanding interest in AI, I've immersed myself in the latest AI advancements in recent years. Passionate about innovation, I actively test AI tools and dedicate myself to guiding users in selecting the right technology. My mission is to connect people with cutting-edge AI solutions.