title: How to do Inference with Custom Preprocessing and Data Files on Google Cloud ML
body: <p>I want to use a model that I have trained for inference on Google Cloud ML. It is a NLP model, and I want my node.js server to interact with the model to get predictions at train time.</p>
  
  <p>I have a process for running inference on the model manually, that I would like to duplicate in the cloud:</p>
  
  <ol>
  <li>Use Stanford Core NLP to tokenize my text and generate data files that store my tokenized text.</li>
  <li>Have the model use those data files, create Tensorflow Examples out of it, and run the model.</li>
  <li>Have the model print out the predictions.</li>
  </ol>
  
  <p>Here is how I think I can replicate it in the Cloud:</p>
  
  <ol>
  <li>Send the text to the cloud using my node.js server.</li>
  <li>Run my python script to generate the data file. It seems like I will have to do this inside of a custom prediction routine. I'm not sure how I can use Stanford Core NLP here.</li>
  <li>Save the data file in a bucket in Google Cloud.</li>
  <li>In the custom prediction routine, load the saved data file and execute the model.</li>
  </ol>
  
  <p>Can anyone tell me if this process is correct? Also, how can I run Stanford CoreNLP on Google Cloud custom prediction routine? Also, is there a way for me to just run command line scripts (for example for creating the data files I have a simple command that I normally just run to create them)?</p>