Welcome to Seahorse!
Here's a quick look at Seahorse's main
view: the palette of operations and the workflow.
Here you can build an application by dragging
operations onto the canvas and defining connections
between them. Each operation is configurable
via its parameters.
Having the Apache Spark as its backend, Seahorse
offers dozens of machine learning algorithms
and data transformations. On top of that,
you're able to define your own transformations
either visually or by embedding code in the
workflow.
Beyond delivering a wide selection of operations,
Seahorse's primary focus is to keep you
in touch with the data you're operating
on. This goal is what drove the design of
the interface and functionality. In particular,
whenever an operation outputs a data set,
you can examine its sample or explore it in
more depth by using a fully functional Jupyter
notebook.
Constructing a workflow is the first of two
steps in the process of developing an application.
The second is deploying the application into
a production environment. Seahorse makes this
effortless: once an application is complete,
it can be exported and run on your cluster.
This brings us to the end of this video.
Thanks for watching!
