Join us as we walk you through H2O Flow, our new open-source user interface for data science with H2O.
H2O Flow is a web-based interactive computational environment where you can combine code execution, text, mathematics, plots and rich media into a single document, much like IPython Notebooks.
Flow not only enables you to use H2O interactively, but also provides you with mechanisms for capturing, replaying, annotating, sharing and presenting your analysis workflow. You can import files, build models, iteratively improve them, make predictions and finally add rich text to build up vignettes of your work for sharing and presentation, all from within Flow’s browser-based environment.
Flow has a “hybrid” user interface that seamlessly blends the concept of a command-line, text-based shell with that of a modern graphical user interface. But where traditional interactive computing environments output text, Flow displays purpose-built point-and-click user interfaces for every operation in H2O. This implies that do not need any computer programming experience to be able to use Flow - it lets you drive H2O’s API interactively and inspect every little detail inside H2O’s data store in the form of nicely arranged and formatted tabular data that you can then further manipulate, analyze and visualize.
In this talk, we will give you a general introduction to H2O and H2O Flow. We will further use examples to demonstrate how H2O Flow can be used by every analytical professional to gain instant insights over big data. We’ll also demonstrate how we build an effective gradient boosting machine model for a marketing campaign problem quickly. The audience can learn how H2O Flow is not only very easy to use, but also powerful when it comes to implemented algorithms. At last, we’d like to encourage you to join us for an interactive hands-on part to start building machine learning models for the demo problem or other problems you are interested in.