Hi, my name is Carthic Ranchan and I'm part of the IoT HT at AWS. I'm here to show you a new starter kit that we have built together with our partners Toradex and NXP. Let's begin by first taking a look at the hardware that we have over here. So what you see over here is a conveyor belt. This conveyor belt, we have glued on it, various kinds of pasta, far-of-elf, penne, elbow. And this conveyor belt is powered by a sawm from Toradex here, which has an NXP IMX8 quad processor. This is the highest class processor that's offered today from NXP within the IMX8 8 line. So what you see here is the pasta moving round and round. Here is a light with a camera up here on top, which is capturing these images of the pasta as they go around. Let's now take a look at what the camera is actually looking at and how our engine works. So here, what you see on the display is the conveyor belt with the pasta actually being detected. Now, the way the system works is that we have a model that's been trained in the cloud using SageMaker. That's then been compiled and very optimized to run on the IMX8 using SageMaker Nea. And that's being delivered using AWS IoT Greengrass. So what you see on the right-hand side here is the pasta. And you'll see the rectangles there showing what the inference engine has actually detected. What kind of pasta it is. So you'll see the label showing elbow, far-off-hell, pen-a, etc. On the left-hand side, what you see is the confidence level. This is the actual algorithm that's doing the machine learning inference to determine how confident it is based on what it sees from the camera. You also see various statistics here, such as CPU load and GPU load. Now let's move and take a look at the cloud dashboard. So through the IMX8, what we now see is a dashboard that's built into AWS that has some of this information stored within DynamoDB where you can actually see, for example, the bar graph here showing in the current frame how many of each kind of pasta has been detected. So for example, we just saw one far-off-hell. And as you see the conveyor belt moving, you're going to see the bar graph pop up showing how many of each kind of pasta has been detected. On the right-hand side of the graph, what you see is the confidence level for the inference engine detection. Let's move on and take a look at some of the other capabilities which are in the cloud dashboard. The other telemetry data that you're getting from the edge is also the inference on how much CPU you're using. If you're monitoring factories remotely through a cloud dashboard, you want to know how these systems are performing. And here you get a complete view of the system. How many cores are being used? Both the small Cortex A53 as well as the large Cortex A72. And then finally, in the last tab, you can actually see the conveyor belt running with the count of the pasta, the speed of the machine, the confidence of the detection. And on the right-hand side, you see the actual MQTT messages that are being delivered to AWS IoT Core from the pasta-debted conveyor belt. So in summary, here we are at the industry of things Berlin showing the AWS IoT connected conveyor belt together with our partners, Toradex and NXP. Thank you.