So we're back here in Custom Vision Portal, and our model was actually quite successful, detecting the difference between the Empire State Building and the Eiffel Tower with 100% certainty for Precision, Recall, and AP. That's pretty good. If I scroll down here on the page, I can also see the individual scores for each tag, as well as the negative tag. This helps me identify if there's one or two classes that I might need to work a bit more on.

Note that these bars are in red. If I mouse over one of them, you see that Custom Vision is telling me that, ideally, I should have at least 50 images per tag for optimal performance. On the top-left of the page, notice that they also have the probability threshold, which is currently at 50%. Let's bring this all the way to 100%, and you see that this brings down the recall, as the model will be less likely to detect a tag, unless it's 100% confident of it. Theoretically, this should also increase my Precision metric, but as it was already at 100%, there's no further improvement in this case. Likewise, if I bring it all the way to zero, now Precision becomes very low. But 50% seems to be the sweet spot for this model, so let's put it back to that.

If I want to test this model, I can just click here on the "Quick Test" button. This opens a new dialog box, where I can either upload a picture or use a URL. As you have already seen the upload option many times, let's paste the URL of the picture of the Empire State Building from its Wikipedia article, and click on the arrow to test it. The model returned the Empire State Building with 99.9% probability, which is pretty good. I can also alternate between iterations of the model, in case I have many of them, so that I can understand whether or not my latest changes are improving the results. Let's close this dialog box, and switch to the Predictions tab.

Here we will have all the predictions made by this model, either using the Portal or the API. And here's the Empire State picture that we have just uploaded and tested. What is fantastic about Custom Vision, is that it allows me to use these past predictions to improve performance. 

Let's do this; I'll click on the image, add Empire State as the tag, and click on "Save and Close." The next time this model is trained, this picture will be used for further improvement of the results, pretty cool, huh? I am now happy with the results, so it's time to publish my model. Let's switch back to the Performance tab, and click here on the Publish button at the top. I'll rename my model to "Latest," make sure my prediction resource is selected, and then click on Publish. After a few seconds, the Publish button switches to "Unpublish," and I now have a "Prediction URL" button enabled for me, which I can use to consume my model through an endpoint, see? Let's see how to consume and export these models next.