TrustRadius Insights for H2O.ai are summaries of user sentiment data from TrustRadius reviews and, when necessary, third party data sources.
Business Problems Solved
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.
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.
Pros
AutoML
Bigdata support with H2O's Sparkling Water
Cons
more state of the art algorithm can be added
Containerization facilities like Docker should be given
Likelihood to Recommend
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.
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.
Pros
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
Cons
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.
Likelihood to Recommend
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.
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.
Pros
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
Cons
Better documentation
Improve the Visual presentations including charting etc
Likelihood to Recommend
<div>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.
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</div><div>Not yet ready for fast, quick and dirty prototyping.
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VU
Verified User
Vice-President in Information Technology (51-200 employees)
