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5. Run the model

Danger

This tutorial is out of date. Please check the tutorials overview for our latest tutorials.

Quix has has already trained model artifacts and these have been included as pickle files in the prediction code application. This application is included in the open source Code Samples. You will use the Code Sample to run the model.

Prediction service code

Get the code for the prediction service:

1. Click on Code Samples in the left-hand navigation.

2. Search for New York and click the New York Bikes - Prediction tile.

3. Click Edit code.

4. Leave the Name as it is.

5. Ensure the bike_input is set to bikes-topic.

6. Ensure the weather_input is set to weather-topic.

7. Ensure the output is set to NY-bikes-prediction.

8. Click Save as Application.

   This will save the code for this service to your environment.

Free Models

Look in the MLModels folder for the Quix pretrained ML models. You can upload your own and compare them to ours. Let us know how they compare.

Run in the dev environment

You can now run the prediction model from this 'dev' environment to make sure it's working before deploying it to an always ready, production environment.

1. Click Run in the top right-hand corner.

2. Observe the Console tab at the bottom of the screen.

   • Any packages that are needed will be installed.

   • Any topics that didn't previously exist will be created.

   • Then the code will run.

   • You will see a line similar to this in the console output.

   Current n bikes: 23742 Forecast 1h: 23663 Forecast 1 day: 22831
   

   Note about data

   For a new prediction to be generated, the service has to receive data from both bikes (updated often) and weather feeds (only updated every 30 mins).

   When you test the model, you may want to force the weather service to produce some new data (to avoid waiting for 30 mins) by restarting the service: stop it and then re-deploy it. By doing this it will start generating predictions sooner.

Deploy the service

Having verified that the code runs, you can now deploy it to the Quix serverless environment. Once deployed, it will run continuously, gathering data from the sources and producing predictions.

1. Click Running to stop the code running.

2. Click Deploy in the top right-hand corner near Run.

3. On the Deployment settings, increase the memory to at least 1.5GB.

4. Click Deploy.

   You will be redirected to the pipeline page and the code will be built, deployed and started.

See the model output

Once the prediction service has started you can once more restart the VisualCrossing Weather service and view the data.

You should be familiar with some of the following steps:

1. Restart the VisualCrossing Weather service.

2. Click Persisted streams in the left-hand menu.

3. Click the toggle switch next to the ny-bikes-prediction topic to persist the data (wait for this to complete).

4. Mouse over the stream name of one of the rows in the table.

5. Click the Visualize stream button.

6. Select both of the parameters (timestamp_ny_prediction and forecast_1d).

7. You can select the Waveform tab to see a graphical representation of the forecast or select the Table tab to see the raw data.

Summary

Congratulations, you have completed all the steps of this tutorial. The following page summarizes your learning and provides some suggestions for next steps to try.

Conclusion and next steps