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H2O.ai
- Last updated: 28 Oct 2024
What is H2O.ai?
H2O.ai provides a comprehensive AI platform offering end-to-end machine learning solutions for various industries. It is equipped with features like automated machine learning and no-code deep learning, ensuring users can easily deploy data-driven applications. H2O.ai supports environments from on-premises to cloud VPC, providing users flexibility and control over their data management.
The platform includes a range of tools such as H2O Driverless AI for machine learning automation and H2O Document AI for intelligent data extraction. By supporting modern AI infrastructure, it allows businesses to innovate across sectors like financial services, healthcare, and manufacturing.
Additionally, H2O.ai’s solutions are designed for scalability, allowing organizations to efficiently harness AI’s power for precise, data-informed decisions, fostering significant value through cost-efficient models and enhanced data security.
H2O.ai use cases
- If you’re a data scientist exploring advanced machine learning models with minimal coding.
- If you’re an IT manager looking to implement scalable AI solutions on-premises or in the cloud.
- If you’re a business analyst aiming to enhance data-driven decision-making processes with automated insights.
H2O.ai functionalities
- Automate Machine Learning: Simplify data science processes.
- Deploy AI Applications: Secure enterprise-level deployment options.
- Data Management Support: Compatibility with on-premises and cloud environments.
- AI Model Fine-Tuning: No-code tools for specialized LLM applications.
H2O.ai review summary
Pros
H2O is frequently praised for its user-friendly interface, rapid setup, and comprehensive machine learning capabilities. Many appreciate its fast performance and the ability to use AutoML and Driverless AI for efficient feature engineering and model optimization. The platform's integration with Python and R makes it accessible to those already familiar with these languages. Additionally, its open-source nature and the extensive support community help users learn and adapt quickly.
Cons
The main drawbacks include insufficient documentation and support, which can make some tasks challenging. The software's cost is often considered high, especially for smaller businesses. Debugging can be complicated due to lack of clarity in error messages. There's also feedback on limited preprocessing capabilities and concerns about model overfitting with small datasets.
Ease of Use and Interface
H2O's interface is simple to navigate, providing a user-friendly experience. It facilitates quick setup and model deployment, making it appealing to both beginners and experienced data scientists.
Documentation and Support
Many users find the documentation lacking and suggest better guidelines and tutorials would enhance the experience.
The tool itself is very intuitive and easy to use. Installation is quick.
Documentation in general can be improved.
AutoML and Driverless AI
The automated feature engineering and model optimization of AutoML and Driverless AI save significant time and effort, providing efficient model comparisons and optimizations.
Cost and Accessibility
The price of Driverless AI and the overall cost structure can be prohibitive, particularly for smaller entities or those new to machine learning.
Driverless AI has strong capability on the auto feature engineering and system visualization.
Price is high for closed source product, Driverless AI.
Integration with R and Python
H2O integrates seamlessly with popular programming languages, facilitating smooth transitions from existing workflows.
Debugging Challenges
Cryptic error messages in H2O-3 make debugging difficult, which can be a hurdle for developers.
The interfaces with R and Python enable a smooth transition of pre-existing workflows into the H2O framework.
Somewhat cryptic debugging msgs in H2O-3.
Performance and Speed
H2O is recognized for its rapid processing speeds and low memory requirements, effectively reducing model training and testing times.
Preprocessing Limitations
There are limitations in data processing capabilities, especially when compared to other tools like Python's pandas.
I really like H2O machine learning and deep learning algorithms... It is really fast and run on really low memory like 2 GB.
H2O Frames have very limited data processing options compared to python pandas or pyspark dataframes.
Pros
H2O is frequently praised for its user-friendly interface, rapid setup, and comprehensive machine learning capabilities. Many appreciate its fast performance and the ability to use AutoML and Driverless AI for efficient feature engineering and model optimization. The platform's integration with Python and R makes it accessible to those already familiar with these languages. Additionally, its open-source nature and the extensive support community help users learn and adapt quickly.
Ease of Use and Interface
H2O's interface is simple to navigate, providing a user-friendly experience. It facilitates quick setup and model deployment, making it appealing to both beginners and experienced data scientists.
The tool itself is very intuitive and easy to use. Installation is quick.
AutoML and Driverless AI
The automated feature engineering and model optimization of AutoML and Driverless AI save significant time and effort, providing efficient model comparisons and optimizations.
Driverless AI has strong capability on the auto feature engineering and system visualization.
Integration with R and Python
H2O integrates seamlessly with popular programming languages, facilitating smooth transitions from existing workflows.
The interfaces with R and Python enable a smooth transition of pre-existing workflows into the H2O framework.
Performance and Speed
H2O is recognized for its rapid processing speeds and low memory requirements, effectively reducing model training and testing times.
I really like H2O machine learning and deep learning algorithms... It is really fast and run on really low memory like 2 GB.
Cons
The main drawbacks include insufficient documentation and support, which can make some tasks challenging. The software's cost is often considered high, especially for smaller businesses. Debugging can be complicated due to lack of clarity in error messages. There's also feedback on limited preprocessing capabilities and concerns about model overfitting with small datasets.
Documentation and Support
Many users find the documentation lacking and suggest better guidelines and tutorials would enhance the experience.
Documentation in general can be improved.
Cost and Accessibility
The price of Driverless AI and the overall cost structure can be prohibitive, particularly for smaller entities or those new to machine learning.
Price is high for closed source product, Driverless AI.
Debugging Challenges
Cryptic error messages in H2O-3 make debugging difficult, which can be a hurdle for developers.
Somewhat cryptic debugging msgs in H2O-3.
Preprocessing Limitations
There are limitations in data processing capabilities, especially when compared to other tools like Python's pandas.
H2O Frames have very limited data processing options compared to python pandas or pyspark dataframes.
H2O.ai user reviews
Quickly build, train and tune your ...
Overall:
Almost every model I have built, firstly I give a chance to H2O and see basic outcomes. After that, I switch to Python and manually buil...
Almost every model I have built, firstly I give a chance to H2O and see basic outcomes. After that, I switch to Python and manually buil...
Overall:
Almost every model I have built, firstly I give a chance to H2O and see basic outcomes. After that, I switch to Python and manually build, tune, train and test my models.
Pros:
I really like H2O machine learning and deep learning algorithms. I use its GUI for preprocessing and analyzing data. I can choose easily model type and tune it via options. It is really fast and run on really low memory like 2 GB. I have learn lotta new information about ML and deep learning thanks to H2O’s help buttons.
Cons:
Some model parameters can not be changed. Also I dom’t like its preprocessing process. It misreads some characters for some file types.
Almost every model I have built, firstly I give a chance to H2O and see basic outcomes. After that, I switch to Python and manually build, tune, train and test my models.
Pros:
I really like H2O machine learning and deep learning algorithms. I use its GUI for preprocessing and analyzing data. I can choose easily model type and tune it via options. It is really fast and run on really low memory like 2 GB. I have learn lotta new information about ML and deep learning thanks to H2O’s help buttons.
Cons:
Some model parameters can not be changed. Also I dom’t like its preprocessing process. It misreads some characters for some file types.
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H2O Rview
Pros:
H2O is a powerful opensource data science and machine learning platform. Most of the ML algorithms are supported and available to use. It's...
H2O is a powerful opensource data science and machine learning platform. Most of the ML algorithms are supported and available to use. It's...
Pros:
H2O is a powerful opensource data science and machine learning platform. Most of the ML algorithms are supported and available to use. It’s easy to launch H2O from R and it noticeably increases the speed of algorithms and reduces time.
Cons:
It would be great if they provide more documentation and guideline.
H2O is a powerful opensource data science and machine learning platform. Most of the ML algorithms are supported and available to use. It’s easy to launch H2O from R and it noticeably increases the speed of algorithms and reduces time.
Cons:
It would be great if they provide more documentation and guideline.
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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.
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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.
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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.
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Expanding AI Product with Excellent...
What do you like best about H2O?
The predictive modeling and machine learning capabilities of this product are top-notch along with their support...
The predictive modeling and machine learning capabilities of this product are top-notch along with their support...
What do you like best about H2O?
The predictive modeling and machine learning capabilities of this product are top-notch along with their support and training. Review collected by and hosted on G2.com.
What do you dislike about H2O?
Documentation in general can be improved. Review collected by and hosted on G2.com.
Recommendations to others considering H2O:
Ask for H2O’s training before you use the software – you’ll have a much better time. Review collected by and hosted on G2.com.
What problems is H2O solving and how is that benefiting you?
We have been able to create features previously undetected and build more accurate prediction models. Review collected by and hosted on G2.com.
The predictive modeling and machine learning capabilities of this product are top-notch along with their support and training. Review collected by and hosted on G2.com.
What do you dislike about H2O?
Documentation in general can be improved. Review collected by and hosted on G2.com.
Recommendations to others considering H2O:
Ask for H2O’s training before you use the software – you’ll have a much better time. Review collected by and hosted on G2.com.
What problems is H2O solving and how is that benefiting you?
We have been able to create features previously undetected and build more accurate prediction models. Review collected by and hosted on G2.com.
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Read less
DriverlessAI
What do you like best about H2O?
H2O provides DriverlessAI for an efficient AutoML platform and effective UI tools for data scientist and end-use...
H2O provides DriverlessAI for an efficient AutoML platform and effective UI tools for data scientist and end-use...
What do you like best about H2O?
H2O provides DriverlessAI for an efficient AutoML platform and effective UI tools for data scientist and end-user. Review collected by and hosted on G2.com.
What do you dislike about H2O?
DriverlessAI could provide more use-cases in Manufacturing domain. Review collected by and hosted on G2.com.
What problems is H2O solving and how is that benefiting you?
We are working on Predictive Maintenance and Analytics, which could be potentially used for Manufacturing and Factory. Review collected by and hosted on G2.com.
H2O provides DriverlessAI for an efficient AutoML platform and effective UI tools for data scientist and end-user. Review collected by and hosted on G2.com.
What do you dislike about H2O?
DriverlessAI could provide more use-cases in Manufacturing domain. Review collected by and hosted on G2.com.
What problems is H2O solving and how is that benefiting you?
We are working on Predictive Maintenance and Analytics, which could be potentially used for Manufacturing and Factory. Review collected by and hosted on G2.com.
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Mr
Overall:
Ran analysis on the data quickly and without having to code anything.
Pros:
This is easy to use. I can quickly run a lot of we...
Ran analysis on the data quickly and without having to code anything.
Pros:
This is easy to use. I can quickly run a lot of we...
Overall:
Ran analysis on the data quickly and without having to code anything.
Pros:
This is easy to use. I can quickly run a lot of well known algorithms without having to code anything at all. The open source nature helps too.
Cons:
The docume.ntation can be improved substantially. Also, need to provide the ability to tweak some algorithms.
Ran analysis on the data quickly and without having to code anything.
Pros:
This is easy to use. I can quickly run a lot of well known algorithms without having to code anything at all. The open source nature helps too.
Cons:
The docume.ntation can be improved substantially. Also, need to provide the ability to tweak some algorithms.
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H2O is value use fully for artifici...
Overall:
H2O is a Strong Performer in Predictive Analytics and Machine Learning.
Pros:
Mostly ai can use or automation work. and H2O.ai...
H2O is a Strong Performer in Predictive Analytics and Machine Learning.
Pros:
Mostly ai can use or automation work. and H2O.ai...
Overall:
H2O is a Strong Performer in Predictive Analytics and Machine Learning.
Pros:
Mostly ai can use or automation work. and H2O.ai is a Strong Performer in Predictive Analytics and Machine Learning.
Cons:
In this software main part is use of that software. H2O not user friendly that way user think for uses.
H2O is a Strong Performer in Predictive Analytics and Machine Learning.
Pros:
Mostly ai can use or automation work. and H2O.ai is a Strong Performer in Predictive Analytics and Machine Learning.
Cons:
In this software main part is use of that software. H2O not user friendly that way user think for uses.
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Great at making machine learning ac...
Pros:
For the machine learning algorithms built into the program, they are incredibly optimizable. Every parameter and hyperparameter of each alg...
For the machine learning algorithms built into the program, they are incredibly optimizable. Every parameter and hyperparameter of each alg...
Pros:
For the machine learning algorithms built into the program, they are incredibly optimizable. Every parameter and hyperparameter of each algorithm is tuneable, and the GUI allows all of this as well.
Cons:
Programmatically using the software is difficult because the documentation is lacking and it is hard to find the documentation that they do have. It’s easier to use the GUI, but that isn’t good for an end-to-end solution.
For the machine learning algorithms built into the program, they are incredibly optimizable. Every parameter and hyperparameter of each algorithm is tuneable, and the GUI allows all of this as well.
Cons:
Programmatically using the software is difficult because the documentation is lacking and it is hard to find the documentation that they do have. It’s easier to use the GUI, but that isn’t good for an end-to-end solution.
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Overall
H2O provides a robust and user-friendly platform for machine learning, admired for its easy integration with R and Python, and efficient AutoML features that save time. You can run complex algorithms quickly with its fast processing speeds. However, the documentation leaves much to be desired, demanding adaptability when seeking guidance. Driverless AI’s effectiveness comes with a steep price, potentially affecting its affordability for smaller teams. Debugging could be less challenging with clearer messages, and the limited preprocessing abilities might constrain some tasks. Prepare for a mixed experience of high functionality and some limitations, especially if budget and documentation are key considerations. The overall sentiment leans positively as H2O continues to be a go-to for machine learning aficionados.
H2O.ai pricing
No pricing is available.
User reviews
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H2O.ai FAQ
What is H2O.ai?
H2O.ai is a company specializing in predictive and generative AI solutions, offering a platform for the development and deployment of AI models both on-premises and in the cloud.
What is the H2O AI Cloud?
The H2O AI Cloud is a complete platform for developing and operationalizing AI and machine learning applications in the cloud, with solutions for different deployment needs.
What products does H2O.ai offer?
H2O.ai offers several products, including H2O Driverless AI, H2O-3, H2O Document AI, H2O Hydrogen Torch, H2O Wave, H2O GPT, and H2O LLM Studio, among others.
Can I deploy H2O.ai solutions on my infrastructure?
Yes, H2O.ai provides options for deploying on your own infrastructure or using their managed cloud solutions, catering to both private and public cloud environments.
What industries can benefit from H2O.ai's solutions?
H2O.ai caters to a wide range of industries, including financial services, healthcare, government, retail, telecommunications, and manufacturing.
Does H2O.ai offer support for open source models?
Yes, H2O.ai provides support for open-source models with solutions like h2oGPT for building generative AI models with enterprise readiness.
What makes H2O Driverless AI unique?
H2O Driverless AI automates the machine learning and data preparation process, allowing users to build sophisticated AI models without writing any code.
Are there educational resources available for H2O.ai users?
Yes, H2O.ai offers a variety of educational resources through H2O University, which includes courses, certifications, and documentation to help users excel in AI.
How does H2O.ai prioritize data security?
H2O.ai’s cloud solutions are SOC2 Type 2 + HIPAA compliant, ensuring high data security standards for all AI operations.
What is the H2O AI AppStore?
The H2O AI AppStore is a platform where users can develop, deploy, and share AI applications safely and reliably with various use cases across different sectors.
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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.