RapidMiner

Gartner Data Analytics Summit 2017
Community Manager
Community Manager
‎03-13-2017 08:13 AM
‎03-13-2017 08:13 AM

Last week I attended the Gartner Data Analytics (DA) summit in Grapevine Texas. It was quite an event, filled with great exhibition booths and presentations. I did booth duty, along with my colleagues, but managed to attend a few great presentations.
 
For three days we staffed our RapidMiner booth and interacted with some fantastic people. Some came from the BI space and were curious about what RapidMiner did, was it BI or somethine else? Other's knew us and wanted to find out more about our Data Science platform.
 
I love these shows, not because I demo the product, but because of the people I have deep conversations with. Roughly 10% of the people I met had some hard implementation problem to overcome. They all understand that data science will solve their problems but getting their team up and running was hard.
 
One guy was a Data Scientist that moved to another company and into a managerial position. His task was to get a DS team up and running at the new firm. He knew the tools out there (i.e. Python, R, etc) but was looking for something he could use (like RapidMiner) to get his new team productive, and quickly.
 
Photos!
 

 

Ingo holding court!Ingo holding court!

 The RapidMiner Booth was hopping!The RapidMiner Booth was hopping!

 

 PerlMonky Wins!

  

On the last day of exhibitions, RapidMiner had a drawing to give away the orange spectacles. We created a RapidMiner process "on the fly" to select a random person on Twitter that tweeted with the hashtags #GartnerDA and #RapidMiner.  Twitter user PerlMonky won the spectacles and here's a post of him wearing them! 

 

And in case you want to see the process we used to build the random drawing? It's below and only three operators long. Now that's Lighting Fast Data Science!

 

<?xml version="1.0" encoding="UTF-8"?><process version="7.4.000">
  <context>
    <input/>
    <output/>
    <macros/>
  </context>
  <operator activated="true" class="process" compatibility="7.4.000" expanded="true" name="Process">
    <process expanded="true">
      <operator activated="true" class="social_media:search_twitter" compatibility="7.3.000" expanded="true" height="68" name="Search Twitter" width="90" x="45" y="34">
        <parameter key="connection" value="ThomasOtt"/>
        <parameter key="query" value="#gartnerda #rapidminer"/>
        <parameter key="limit" value="1000"/>
      </operator>
      <operator activated="true" class="filter_examples" compatibility="7.4.000" expanded="true" height="103" name="Filter Examples" width="90" x="179" y="34">
        <parameter key="invert_filter" value="true"/>
        <list key="filters_list">
          <parameter key="filters_entry_key" value="From-User.equals.Ingo Mierswa"/>
          <parameter key="filters_entry_key" value="From-User.equals.Thomas Ott"/>
          <parameter key="filters_entry_key" value="From-User.equals.Tom Wentworth"/>
          <parameter key="filters_entry_key" value="From-User.equals.RapidMiner"/>
        </list>
        <parameter key="filters_logic_and" value="false"/>
      </operator>
      <operator activated="true" class="sample" compatibility="7.4.000" expanded="true" height="82" name="Sample" width="90" x="313" y="34">
        <parameter key="sample_size" value="1"/>
        <list key="sample_size_per_class"/>
        <list key="sample_ratio_per_class"/>
        <list key="sample_probability_per_class"/>
        <parameter key="use_local_random_seed" value="true"/>
        <parameter key="local_random_seed" value="2000"/>
      </operator>
      <connect from_op="Search Twitter" from_port="output" to_op="Filter Examples" to_port="example set input"/>
      <connect from_op="Filter Examples" from_port="example set output" to_op="Sample" to_port="example set input"/>
      <connect from_op="Sample" from_port="example set output" to_port="result 1"/>
      <portSpacing port="source_input 1" spacing="0"/>
      <portSpacing port="sink_result 1" spacing="0"/>
      <portSpacing port="sink_result 2" spacing="0"/>
    </process>
  </operator>
</process>