H2O Reviews

6 Ratings
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Score 9.1 out of 101

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September 18, 2019

H2O AutoML superb!!

Score 8 out of 10
Vetted Review
Verified User
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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.
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Viktor Mulac profile photo
Score 10 out of 10
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Verified User
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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.
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Score 10 out of 10
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Verified User
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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.
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Feature Scorecard Summary

Connect to Multiple Data Sources (1)
8
Automatic Data Format Detection (1)
8
Visualization (1)
8
Interactive Data Analysis (1)
9
Interactive Data Cleaning and Enrichment (1)
10
Data Transformations (1)
9
Built-in Processors (1)
9
Multiple Model Development Languages and Tools (1)
10
Automated Machine Learning (1)
10
Single platform for multiple model development (1)
10
Self-Service Model Delivery (1)
10
Flexible Model Publishing Options (1)
10
Security, Governance, and Cost Controls (1)
8

About H2O

H2O.ai is an open-source predictive analytics and machine learning platform.

H2O Technical Details

Operating Systems: Unspecified
Mobile Application:No