H2O.ai vs. Logi Info

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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Logi Composer is the best of the product range that Logi Analytics offers; Logi Info is, in my mind, a terrible experience and should be discontinued. Logi Info is not a BI tool and many CEOs and product managers believe it is. Instead, it is a tool for web developers to create IIS apps with a library of BI-type components, and with everything else having to be hand-coded in JavaScript and CSS. Maybe this was cool back in the early 2000s when Logi Info came about, but today I'd recommend using Visual Studio, C#, .NET, and finding a NuGet package for your visualisations if you wanted to go the route of making your own web app. This said, where it is suited is if you have a burning desire to make a stand-alone IIS-based web application and you don't need or want to leverage any existing skills in .NET or PHP or other frameworks/languages. Instead, you want to use the Logi Info XML-based BI widgets and you're happy to make something quick without needing it to look really awesome.

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• 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

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• Clear/Concise view of data. Easily intuitive, amazing drill down capability, fully user configurable panels and dashboards.
• Self-Service is such a powerful tool for facilities that want to create/design their own reports/dashboards, it's instantly recognized as being very powerful and agile.
• Logi software makes the designer realize that they should think outside the box and not stick with "it's how we have always done it limitation". It allows you to be very creative.

• Better documentation
• Improve the Visual presentations including charting etc

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• It is not always intuitive to incorporate JavaScript into Logi Info. The tools are all available, but the process requires multiple resource in translating between Logi and JavaScript.
• The toolbox is too vast for Logi to provide a 'quick implementation' or 'quick start guide'. There are so many tools that Logi expects the client to determine which fit their use case. There are purchasable 'Professional Services' options for implementation, but without these, the toolset is almost too big.

Logi Info is a very outdated, archaic product that tries to build .NET / Java web apps using an obscure XML-based markup language to implement BI widgets, with a lot of extra CSS/JavaScript needed on your own to make it do the best things. There are many other better tools. It is not a BI tool, and as a web development tool it's not great either. I'd recommend getting some good third-party .NET BI library if you want your web devs to make the reports, otherwise use a proper BI tool like Power BI or Tableau, or even Logi Composer (formerly ZoomData before Logi acquired it.)

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I am giving 9 rating because the Logi Info still needs to improve on the tutorials part and make it easy for the beginners. Otherwise, it's a very good analytics tool which offers more than 20 types of visualization. It's predictive analysis feature and easy to embed with technologies make it stand out in the market.

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The overall experience I have with H2O is really awesome, even with its cost effectiveness.

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The support process is bit slow and has a good scope improvement but overall it's good as team is very supportive. They generally take 1-2 days time to respond emails sent to them but some times a delay is also expected. Overall, I did not face any major issues using the service.

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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.

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Logi gave us the flexibility we needed to meet the configurable nature of our product and for the need to create custom reports. Other products did not allow flexbility to generate reports via script as was one of our primary requirements.

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• 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

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• By embedding Logi in our solution and using the Logi Self-Service Module we can provide this flexibility to our users without requiring custom development work for each new request.
• We succeeded in developing embedded self-service analytics at scale with a combination of Logi analytics as front-end and a Cassandra data lake with Spark aggregation algorithms as back-end.
• We analyze the insurance industry and need to replicate different data formats across hundreds of databases to support multi-tenant (customer) BI reports and "ad hoc" data review on millions or hundreds of millions of records per customer.

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A de minimis incentive was given to thank the reviewer for their time. The incentive was not used to bias or drive a particular response, nor was the incentive contingent on a positive endorsement. More Info