vijaydairyf / cloud-computing-project Goto Github PK

set up predictive service as such https://docs.microsoft.com/en-us/azure/machine-learning/studio/tutorial-part3-credit-risk-deploy make sure you go to prediction in experiment > deploy as predictive web app in the web app UI, you should see [predictive]

=================================================== to retrain model, see retrain2.py for script that the controller interface should use to access predictions, see request-response api in the ml web services to update model for predictions, see update.py.. not working yet

Our folder structure is as follows:

• preprocessing: contains python scripts we used to preprocess the data. and the original data file.
  
• fake_cow_data: contains a csv file for each cow.
  
• fake_cow_data_with_birth: contains a csv for each cow. Cows that gave birth during the 120 periods we have data for have the line "BIRTH" appended to their csv.
  
• evaluation: contains our evaluation script and stress testing script.
  
• sensors: contains files that: declare our sensors, register them with the IoT hub, and allow them to send data from the cow files. Do node run.js in the terminal to send data from our sensors.
  
• function-servers: contains our files related to our function server on Azure. The folder named dataReceivedFunction has a file named index.js that is the code in our function on the function server.
  
• stream-analytics: contains a template file that shows our setup in Stream Analytics.
  
• ml: contains our predictive and training ML code.