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H2O.ai

H2O.ai

Overview

What is H2O.ai?

An open-source end-to-end GenAI platform for air-gapped, on-premises or cloud VPC deployments. Users can Query and summarize documents or just chat with local private GPT LLMs using h2oGPT, an Apache V2 open-source project. And the commercially available Enterprise h2oGPTe…

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Recent Reviews

TrustRadius Insights

H2O.ai has proven to be a valuable tool for a variety of use cases across different domains. Users have successfully employed the software …
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H2O AutoML superb!!

8 out of 10
September 18, 2019
Incentivized
We use H2O.ai for building End to End auto pipelines for machine learning models. It has massively good support with big data. For that we …
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Pricing

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What is H2O.ai?

An open-source end-to-end GenAI platform for air-gapped, on-premises or cloud VPC deployments. Users can Query and summarize documents or just chat with local private GPT LLMs using h2oGPT, an Apache V2 open-source project. And the commercially available Enterprise h2oGPTe provides information…

Entry-level set up fee?

  • No setup fee

Offerings

  • Free Trial
  • Free/Freemium Version
  • Premium Consulting/Integration Services

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Product Demos

Make a GPT With h2oGPT & H2O LLM Studio

YouTube

H2O Quick Start with R

YouTube

H2O Quick Start with Python

YouTube

H2O Driverless AI Demo

YouTube
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Product Details

What is H2O.ai?

An open-source end-to-end GenAI platform for air-gapped, on-premises or cloud VPC deployments. Users can Query and summarize documents or just chat with local private GPT LLMs using h2oGPT, an Apache V2 open-source project. And the commercially available Enterprise h2oGPTe provides information retrieval on internal data, privately hosts LLMs, and secures data.

H2O AI Cloud is a cloud-based option providing the same capabilities, either managed by H20.ai (the Managed Cloud version), and the self-hosted hybrid cloud edition. H20.ai presents the Managed Cloud edition as a SOC2 Type 2 + HIPAA compliant H2O AI Cloud powered by AutoML and no-code deep learning engines, and a end-to-end fully managed data science and machine learning platform, available without the day to day operations or maintenance of running a scalable Kubernetes cluster.

H2O.ai Video

Democratizing AI with the H2O AI Cloud

H2O.ai Integrations

H2O.ai Technical Details

Deployment TypesOn-premise, Software as a Service (SaaS), Cloud, or Web-Based
Operating SystemsLinux
Mobile ApplicationNo
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Comparisons

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Reviews and Ratings

(14)

Community Insights

TrustRadius Insights are summaries of user sentiment data from TrustRadius reviews and, when necessary, 3rd-party data sources. Have feedback on this content? Let us know!

H2O.ai has proven to be a valuable tool for a variety of use cases across different domains. Users have successfully employed the software for forecasting prices using regression models, allowing them to quickly test and experiment with new models. The AutoML feature of H2O.ai has been highly beneficial in developing ML/AI prototype solutions in various industries, providing users with a quick and efficient way to build models. Additionally, H2O.ai has been utilized in creating a Policy Lapse Predictor, automating the model tuning process and delivering significant benefits.

Furthermore, H2O.ai has addressed the need for adaptable machine learning analyses by offering a plug-and-play solution for users. It has proven effective in solving complex problems in academic research and healthcare. Time-series data analysis and stock market prediction have also been successfully performed using H2O.ai. In the field of predictive maintenance, H2O.ai simplifies the process for system operators by enabling data analytics. Users have found H2O.ai useful for tasks such as purchase forecasting, employee estimation, credit prediction, marketing analytics, and assortment optimization.

The software has enabled users to create previously undetected features and develop more accurate prediction models. It streamlines the process of generating and deploying machine learning models, enhancing efficiency. For AdTech modeling, H2O.ai allows users to create complex models on large datasets with faster turnaround times. Users often start with H2O.ai for basic model outcomes before switching to Python for more manual model building and tuning. The ease of use and accessibility of H2O.ai have made it a popular choice among beginners in AI and data analysis.

Overall, H2O.ai has received positive feedback for its strong performance in predictive analytics and machine learning. It provides accessible functionalities for data analysis and modeling, making it widely used across organizations for various business purposes. Whether it's measuring the Return on Ad Spend ROAS for advertisers or serving as a core tool for media companies, H2O.ai continues to serve as a valuable asset in driving data-driven decision-making.

Users have made several recommendations for H2O.ai DriverlessAI based on their experience with the platform. The most common recommendations include:

  1. H2O.ai DriverlessAI is highly recommended for companies that do not have data scientists. Users appreciate how the platform automates the machine learning process, making it accessible to non-technical users without extensive data science knowledge.

  2. Users suggest taking advantage of the training resources provided by H2O.ai before using the software. They find that training helps them better understand the platform's capabilities and achieve optimal results.

  3. Reviewers often recommend using H2O.ai DriverlessAI to establish a machine learning pipeline easily. They appreciate how the platform simplifies the end-to-end process of developing and deploying machine learning models, integrating AI capabilities into existing workflows and applications.

Overall, these recommendations highlight how H2O.ai DriverlessAI can benefit companies without data scientists, the importance of training before using the software, and its ease of use in setting up an efficient machine learning pipeline.

Attribute Ratings

Reviews

(1-3 of 3)
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September 18, 2019

H2O AutoML superb!!

Score 8 out of 10
Vetted Review
Verified User
Incentivized
We use H2O.ai for building End to End auto pipelines for machine learning models. It has massively good support with big data. For that we use H2O's Sparkling Water. As far as I have experienced, H2O gives the highest accuracy among all other autoML tools. I have used it in our one of the projects and I had to deliver in just 1 week. Building an ML model with H2O, as well as fast training and auto tuning, helped me a lot.
  • AutoML
  • Bigdata support with H2O's Sparkling Water
  • more state of the art algorithm can be added
  • Containerization facilities like Docker should be given
Most suited if in little time you wanted to build and train a model. Then, H2O makes life very simple. It has support with R, Python and Java, so no programming dependency is required to use it. It's very simple to use.
If you want to modify or tweak your ML algorithm then H2O is not suitable. You can't develop a model from scratch.
I have used Knime, RapidMiner, and Weka before I heard about H2O, but amongst all I really liked H2O. However, nowadays Googles AutoML and AWS SageMaker AutoML platform are really competitive, but more costly than H2O.
The overall experience I have with H2O is really awesome, even with its cost effectiveness.
Score 10 out of 10
Vetted Review
Verified User
Incentivized
H2O was used as an analytical tool, with easy to access machine learning functionalities. The data science team comprises different people with different backgrounds and abilities to code. We used H2O as an easily trained on, highly accessible tool for beginners in the AI area. As an open source version, it is good for small projects and trials in data analysis, scoring, clustering, and predictive modeling. It is a really fast tool and also runs on older hardware.
  • Excellent analytical and prediction tool
  • In the beginning, usage of H20 Flow in Web UI enables quick development and sharing of the analytical model
  • Readily available algorithms, easy to use in your analytical projects
  • Faster than Python scikit learn (in machine learning supervised learning area)
  • It can be accessed (run) from Python, not only JAVA etc.
  • Well documented and suitable for fast training or self studying
  • In the beginning, one can use the clickable Flow interface (WEB UI) and later move to a Python console. There is then no need to click in H20 Flow
  • It can be used as open source
  • No weaknesses found yet
  • This is not really a drawback, but rather a warning - the Drivereless AI is not a replacement for a data scientist yet, and will not replace data scientists in the next decade neither. The Driverless AI feature delivers reliable results only if the analyst is sure about the meaning of input data. The data quality is usually a major issue and no tool can detect the meaning of data in the input. Data scientists are also required for business interpretation of the findings. So be careful, and do not rely on this feature without a good understanding of what it really does in each step.
Use H2O.ai whenever you need easy to use tool, when you must be cost efficient (you can not charge the client extra money for software licenses used), need a tool with lots of algorithms that are normally used in data analytics, or need to work on one machine (it is either not allowed to move data to cloud storage or simply not necessary to connect to Hadoop, etc.). Also, you can call H2O directly from Python which makes analysis more efficient.
  • By using H2O the analyst can focus on analysis itself, not spend too much time with coding etc.
  • Reuse of algorithms and easy model sharing saves time and money
  • An easy learning curve assures low training costs
  • By moving to a paid version, even the Driverless AI, you will still need data scientists and analysts, but maybe not so many!
Both are open source (though H2O only up to some level). Both comprise of deep learning, but H2O is not focused directly on deep learning, while Tensor Flow has a "laser" focus on deep learning. H2O is also more focused on scalability. H2O should be looked at not as a competitor but rather a complementary tool. The use case is usually not only about the algorithms, but also about the data model and data logistics and accessibility. H2O is more accessible due to its UI. Also, both can be accessed from Python. The community around TensorFlow seems larger than that of H2O.
Score 10 out of 10
Vetted Review
Verified User
Incentivized
H2O is used as a core tool across the whole organization. The primary business we are in is measuring the Return on Ad Spend (ROAS) for advertisers, media companies and CPG marketing and product companies.
  • Flexible modeling including Ensemble
  • Open Source - so that we can know what is really happening and can request changes when needed
  • Ability to scale up horizontally by provisioning dynamic clusters
  • Access to core development team and speed of problem resolution and feature additions
  • Better documentation
  • Improve the Visual presentations including charting etc
It is able to handle large amounts of data. It is best suited when we want to productionalize BI and Analytical applications/features with ease and scale well. Applicable for ensemble learning, data munging, scaled application development.

Not yet ready for fast, quick and dirty prototyping.
  • Positive impact: saving in infrastructure expenses - compared to other bulky tools this costs a fraction
  • Positive impact: ability to get quick fixes from H2O when problems arise - compared to waiting for several months/years for new releases from other vendors
  • Positive impact: Access to H2O core team and able to get features that are needed for our business quickly added to the core H2O product
H2O provided all the needed features such as Linear Modeling, Targeted Learning, Predictive Analytics including GLM, Trees, Neural networks and ensemble with ease. We are also able to pick and choose what we want without deploying all the bulky tools unlike others. Able to package H2O jar with our home grown code for remote deployments without worrying able expensive licenses.
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