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H2O.ai is an open-source predictive analytics and machine learning platform.Fast machine learning with H2OH2O 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.,10,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!,TensorFlowCustom H2O implementationH2O 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,10,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,SAP Predictive Analytics and SAS Advanced Analytics,SAS Business Intelligence
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H2O
5 Ratings
Score 9.4 out of 101
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H2O Reviews

H2O
5 Ratings
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Score 9.4 out of 101
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Viktor Mulac profile photo
November 26, 2018

User Review: "Fast machine learning with H2O"

Score 10 out of 10
Vetted Review
Verified User
Review Source
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.
Read Viktor Mulac's full review
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August 16, 2018

User Review: "Custom H2O implementation"

Score 10 out of 10
Vetted Review
Verified User
Review Source
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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H2O Scorecard Summary

About H2O

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

H2O Technical Details

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Mobile Application:No