Publishing an MLMessage with a message type of GetResult will get the volume (tensor) probability of the network at the point it is currently at. In our example at the start of the chapter, this should give us a probability of somewhere around 0.501.

With the machine learning messages flowing, we now move on to take a look at what our RabbitMQ message queue looks like. If we are lucky, and we are, we will have message traffic. The following is what our machine learning queue will look like once we begin processing messages. You will notice that there are 23 messages already in our queue. This tells us that we have no consumers actively looking for our microservice or the message level would be at zero because the microservice would have consumed all of the messages. So, if you see messages in your queue, much like water on your floor, something's wrong!