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From dusenberr...@gmail.com
Subject Re: Documentation Personas
Date Fri, 30 Sep 2016 00:51:50 GMT
These topics and the idea of the personas are great!  I would suggest that we don't explicitly
use the specific persona titles though. I.e. Instead of specifically using "data scientists"
we could just use "machine learning" such as "Getting started guide to machine learning with
SystemML". Likewise the topics for "data engineers" could instead be something like "Getting
started guide to engine development". We could also have a "deployment" guide, etc.  In each
we could also have beginner and expert sections.

--

Mike Dusenberry
GitHub: github.com/dusenberrymw
LinkedIn: linkedin.com/in/mikedusenberry

Sent from my iPhone.


> On Sep 29, 2016, at 4:07 PM, Luciano Resende <luckbr1975@gmail.com> wrote:
> 
> On Wed, Sep 28, 2016 at 6:06 PM, Madison Myers <madisonjmyers@gmail.com>
> wrote:
> 
>> Thanks for opening up this dialogue Felix & Luciano!
>> .....
>> 
>> Additionally, Luciano mentioned that he was trying to find two key personas
>> for the SystemML website. Looking at the results from the SystemML survey I
>> sent out a few weeks ago, it is clear that an overwhelming majority of
>> people voted for Data Scientist- new and Data Scientist- advanced. Input on
>> if you agree on these results would be helpful.
>> 
>> ....
>> 
>> Thanks again!
>> Madison
> 
> 
> 
> I was thinking on two main personas : Data Scientist and Data Engineers.
> And at least for the Data Scientist, we might have to have topics for both
> R and Python.
> 
> 
> In summary, I would say :
> 
> 
> 
> Getting Started for Data Scientists (note that we should have a version for
> R and Python):
> 
> - Creating algorithms with SystemML : this is kind a helloworld where a
> Data Scientist creates and runs a very simple algorithm.
> 
> - Running existing algorithms with SystemML : in this scenario, we would
> guide the data scientist on how to run an algorithm from the sample library
> from SystemML. We should also guide the data scientist to choose the right
> algorithm for his/her specific problem.
> 
> - Customizing existing algorithms with SystemML: in this scenario, we would
> guide the data scientist to start making customization and applying the
> algorithm for different data sets.
> 
> 
> 
> 
> Getting started for Data Engineers:
> 
> - Overview of different options/runtimes supported by SystemML
> 
> - Debugging SystemML ???
> 
> - Engine development guide ???
> 
> Thoughts ?
> 
> 
> -- 
> Luciano Resende
> http://twitter.com/lresende1975
> http://lresende.blogspot.com/

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