Table of Contents

1. Challenge

   Open the Azure ML Studio and Create a New Dataset

   In the Azure ML studio, create a new dataset using the taxi fare data file from Microsoft.

2. Challenge

   Configure and Start the Automated ML Run

   Change the timeout of the job to 15 minutes by expanding limits on the task settings page, and set the Experiment timeout (minutes) to 15, so it will stop after approximately 15 minutes.

3. Challenge

   Review Results

   Take a look at some of the metrics for the run, like the R2 score, or the root mean squared error.

4. Challenge

   Deploy the Model to a Real-time Endpoint

   Make sure you change the virtual machine to a DS2_v2 size and change the instance count to 1, or your resources will be deleted.

5. Challenge

   Test Your Newly Deployed Model

   • Use the test tab and input the JSON data below to test your model and see how accurate the predicted value is compared to the actual value of 17.

     {
        "input_data":{
           "data":[
              {
                 "vendor_id":"CMT",
                 "rate_code":1,
                 "passenger_count":1,
                 "trip_time_in_secs":1271,
                 "trip_distance":3.8,
                 "payment_type":"CRD"
              }
           ]
        }
     }