title: IBM Watson NLC - Training with more than 20,000 text examples?
body: <p>We're currently developing a system that would return an ICD10-CM code (A medical/diagnosis coding system) from a text input. Example</p>
  
  <ul>
  <li>input 'Black Eye'</li>
  <li>return 'H44 - Disorders of the globe'</li>
  </ul>
  
  <p>Problem is, ICD10-CM has 70,000 to 100,000 codes, so it won't let me train the model after I uploaded all those text examples from .csv files.</p>
  
  <p>Is using multiple models a solution or should I switch to Google's AutoML?</p>