Adding images and using new Azure icons

.vscode/settings.json

@@ -10,6 +10,7 @@
         "UDID",
         "Zigbee",
         "antimeridian",
+        "architecting",
         "geofence",
         "geofences",
         "geofencing",

4-manufacturing/lessons/1-train-fruit-detector/README.md

@@ -90,6 +90,8 @@ There are a wide range of tools that can help you do this, including cloud-based
 
 Custom Vision is a cloud based tool for training image classifiers. It allows you to train a classifier using only a small number of images. You can upload images through a web portal, web API or an SDK, giving each image a *tag* that has the classification of that image. You then train the model, and test it out to see how well it performs. Once you are happy with the model, you can publish versions of it that can be accessed through a web API or an SDK.
 
+![The Azure Custom Vision logo](../../../images/custom-vision-logo.png)
+
 > 💁 You can train a custom vision model with as little as 5 images per classification, but more is better. You can get better results with at least 30 images.
 
 Custom Vision is part of a range of AI tools from Microsoft called Cognitive Services. These are AI tools that can be used either without any training, or with a small amount of training. They include speech recognition and translation, language understanding and image analysis. These are available with a free tier as services in Azure.

4-manufacturing/lessons/2-check-fruit-from-device/README.md

@@ -151,7 +151,7 @@ If you were to create a production device to sell to farms or factories, how wou
 
 You trained your custom vision model using the portal. This relies on having images available - and in the real world you may not be able to get training data that matches what the camera on your device captures. You can work round this by training directly from your device using the training API, to train a model using images captured from your IoT device.
 
-* Read up on the training API in the [Using the Custom Vision SDK quickstart](https://docs.microsoft.com/azure/cognitive-services/custom-vision-service/quickstarts/image-classification?tabs=visual-studio&pivots=programming-language-python&WT.mc_id=academic-17441-jabenn)
+* Read up on the training API in the [Using the Custom Vision SDK quick start](https://docs.microsoft.com/azure/cognitive-services/custom-vision-service/quickstarts/image-classification?tabs=visual-studio&pivots=programming-language-python&WT.mc_id=academic-17441-jabenn)
 
 ## Assignment
 

4-manufacturing/lessons/3-run-fruit-detector-edge/README.md

@@ -2,7 +2,9 @@
 
 Add a sketchnote if possible/appropriate
 
-![Embed a video here if available](video-url)
+This video gives an overview of running image classifiers on IoT devices, the topic that is covered in this lesson.
+
+[![Custom Vison AI on Azure IoT Edge](https://img.youtube.com/vi/_K5fqGLO8us/0.jpg)](https://www.youtube.com/watch?v=_K5fqGLO8us)
 
 ## Pre-lecture quiz
 

4-manufacturing/lessons/4-trigger-fruit-detector/README.md

@@ -10,13 +10,37 @@ Add a sketchnote if possible/appropriate
 
 ## Introduction
 
-In this lesson you will learn about
+An IoT application is not just a single device capturing data and sending it to the cloud, it is more often that not multiple devices all working together to capture data from the physical world using sensors, make decisions based off that data, and interacting back with the physical world via actuators or visualizations.
+
+In this lesson you will learn more about architecting complex IoT applications, incorporating multiple sensors, multiple cloud services to analyze and store data, and showing a response via an actuator. You will piece together a more advanced fruit quality tracking system.
 
 In this lesson we'll cover:
 
-* [Thing 1](#thing-1)
+* [Architect complex IoT applications](#architect-complex-iot-applications)
+* [Design a fruit quality control system](#design-a-fruit-quality-control-system)
+* [Trigger fruit quality checking from a sensor](#trigger-fruit-quality-checking-from-a-sensor)
+* [Store fruit quality data](#store-fruit-quality-data)
+* [Control feedback via an actuator](#control-feedback-via-an-actuator)
+
+## Architect complex IoT applications
+
+![A reference iot architecture](../../../images/iot-reference-architecture.png)
+
+***A reference iot architecture. LED by abderraouf omara / Microcontroller by Template - all from the [Noun Project](https://thenounproject.com)***
+
+## Design a fruit quality control system
+
+![A reference iot architecture for fruit quality checking](../../../images/iot-reference-architecture-fruit-quality.png)
+
+***A reference iot architecture for fruit quality checking. LED by abderraouf omara / Microcontroller by Template - all from the [Noun Project](https://thenounproject.com)***
+
+## Trigger fruit quality checking from a sensor
+
+### Task - trigger fruit quality detection from a distance sensor
+
+## Store fruit quality data
 
-## Thing 1
+## Control feedback via an actuator
 
 ---