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.
Read full review MATLAB really does best for solving computational problems in math and engineering. Especially when you have to use a lot of functions in your solving process, or if you have a nonlinear equation that must be iteratively solved. [MATLAB] can also perform things like integration and derivation on your equations that you put into it.
Read full review 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 Read full review It has a very user friendly library which helps users learn this software fairly quickly in a short span of time. The graphical user interface provided by the software is really good. The code that a person writes allows options for debugging. One can visualize the flow of control of their code inside MATLAB. Read full review Cons Better documentation Improve the Visual presentations including charting etc Read full review MatLab is pricier than most of its competitors and because of this reason, many organizations are moving towards cheaper alternatives - mostly Python. MatLab is inefficient when it comes to performing a large number of iterations. It gets laggy and often crashes. Python is better in this regard. There is a limited number of hardware options (mostly NI) that can be connected directly to the data acquisition toolbox. Read full review Usability MATLAB is pretty easy to use. You can extend its capabilities using the programming interface. Very flexible capabilities when it comes to graphical presentation of your data (so many different kinds of options for your plotting needs). Anytime you are working with large data sets, or with matrices, MATLAB is likely to be very helpful.
Read full review Support Rating The overall experience I have with H2O is really awesome, even with its cost effectiveness.
Read full review The built-in search engine is not as performing as I wish it would be. However, the YouTube channel has a vast library of informative video that can help understanding the software. Also, many other software have a nice bridge into MATLAB, which makes it very versatile. Overall, the support for MATLAB is good.
Read full review Alternatives Considered 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.
Read full review How MATLAB compares to its competition or similar open access tools like R (programming language) or SciLab is that it's simply more powerful and capable. It embraces a wider spectrum of possibilities for far more fields than any other environment. R, for example, is intended primarily for the area of statistical computing. SciLab, on the other hand, is a similar open access tool that falls very short in its computing capabilities. It's much slower when running larger scripts and isn't documented or supported nearly as well as MATLAB.
Read full review Return on Investment 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 Read full review MATLAB helps us quickly sort through large sets of data because we keep the same script each time we run an analyzation, making it very efficient to run this whole process. The software makes it super easy for us to create plots that we can then show to investors or clients to display our data. We are also looking to create an app for our product, and we will not be able to do that on MATLAB, therefore creating a limiting issue and a new learning curve for a programming language. Read full review ScreenShots