H2O AutoML superb!!
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 …
H2O
H2O
H2O
Overview
What is H2O?
H2O.ai is an open-source predictive analytics and machine learning platform.
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What is H2O?
H2O.ai is an open-source predictive analytics and machine learning platform.
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- No setup fee
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- Free Trial
- Free/Freemium Version
- Premium Consulting / Integration Services
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Product Demos
H2O Driverless AI Demo
06:02
Product Details
- About
- Tech Details
What is H2O?
H2O.ai is an open-source predictive analytics and machine learning platform.
H2O Video
Watch this end-to-end demo of H2O Driverless AI.
This demo includes:
(1) Data Visualization
(2) An AI experiment
(3) Machine Learning Interpretability
(4) One-click deployment
(5) Bring Your Own Recipe
This demo gives you the perfect overview in just over 6 minutes!
H2O Technical Details
| Operating Systems | Unspecified |
|---|---|
| Mobile Application | No |
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Reviews and Ratings
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September 18, 2019
H2O AutoML superb!!
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
80%
8.0
85%
8.5
93.33333333333334%
9.3
100%
10.0
90%
9.0
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.
November 26, 2018
Fast machine learning with H2O
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.
- 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.
August 16, 2018
Custom H2O implementation
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
- 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.