 Hi, my name is Michael Lamp. I'm with AvNet and I'm here to talk to you about a product called Agile. Agile is brought to us by AvNet, ST, and a company called Octonian who brings edge intelligence to the edge. What we are showing today is a fully certified IP67 waterproof CE and FCC compliant module that you can use in your proof of concepts all the way through a proof of value and deliver into production for your products. What we have done is we have incorporated ST's STM32 microcontrollers, their sensors, as well as their connectivity into a product. This product alongside AvNet's hardware capabilities enables you to go to market much quicker than you could from scratch. So I'm going to connect it to a motor. And in this use case what I'm trying to do is understand the normal operation of a motor so that I can then train and then search for anomalies whenever there's something wrong. This is a predictive maintenance type application. So the first thing I'm going to do is I'm going to turn on the motor and wait till it gets up at speed. So that's where I know that the motor is in normal operation. Once the motor is at speed I want to then train my algorithm based on normal operation of this device. So now I'm going to go ahead and activate the Branium Studio software. I'm going to connect to my device. So now I'm communicating directly to the device over USB. In some scenarios or most scenarios I'd probably be connected through a gateways, through BLE, through a gateway directly into the cloud. But for the purposes of this demonstration we're going to be communicating through the USB-C port of the device. So I've chosen to do 50,000 iterations or 50,000 data points on the device. And I'm going to start learning each of those 50,000 points, pull that data in and then process that and create my algorithms or my model so that I can then export that back onto my device in the edge. So now we've taken all those data points, 50,000 data points. In some applications we may have had 600,000 data points. I'm going to take those and I'm going to start my machine learning process. So the model is being generated right now. It's going through understanding what the machine learning is. It's going to process that data so that it can be relevant to an M4 processor from ST. It's going to generate that model based on the inputs that we have provided from running at the edge. And once that model has been produced I'm going to be able to download that back into the device. The model has been generated so now I'm going to download that back into my device at the edge. So based on the models we can see that the device is communicating BLE to my dashboard. In my dashboard you can see that the motor is operating in the good range so it's acceptable. The motor has no issues. But what happens when we start introducing problems to the motor? In this case I'm going to start introducing weights to the motor to cause more vibration. And according to these ISO models would be out of spec and show potential damage to my motor. So the first thing I'm going to do is I'm going to turn off my motor. I'm going to add some weights. And what you'll see is the motor will go into the unhealthy state. What Brainiam is telling me is that I'm a 100% chance of damaging my motor at this rate of vibration based on these ISO models. But what's really important about this demo is the fact that I can make a split-second decision based on this data from the Brainiam device. So instead of just talking directly from the device to the cloud I'm going to have the device talk to the motor as well and tell it to turn off based on these levels. So now the device is directly connected to the motor and it's basically telling me it's going to harm itself and in five seconds it's going to shut down. As you can see it's shut down the motor. So if I go here and I correct the problem by removing the weights and disconnect Brainiam from the device it will go ahead and perform back at normal operation. And if you'd like to learn more about the Brainiam technology and what Avnet can bring to artificial intelligence please visit our websites or visit one of your local salespeople.