Authoring for Pluralsight – Azure Machine Learning

Off to start another set of courses for Pluralsight:

Sourcing Data in Microsoft Azure
Deploying and Managing Models in Microsoft Azure
Cleaning and Preparing Data in Microsoft Azure

If you would like to check out any of my other courses, visit my author’s profile.

Sourcing Data in Microsoft Azure

This course is for people looking to move into the data sciences. They can have an existing background in development or IT.

This course will show how to find data in Microsoft Azure, how to move and change that data, and finally how to build workflows around that data.

This course assumes the developer has an understanding of basic computer terminology and the azure portal.

Deploying and Managing Models in Microsoft Azure

This course is for people looking to move into the data sciences. They can have an existing background in development or IT.

This course introduces the audience to the different data preparation steps involved with data projects. This course will show how to clean, transform, and wrangle the data needed for a data project.

This course assumes the developer has an understanding of basic computer terminology and the azure portal.

Cleaning and Preparing Data in Microsoft Azure

This course is for data science practitioners who need to learn more about how to utilize tools for managing the models they create.

The audience will be taken through automation and DevOps to learn more about how to manage their workflows. Everything from versioning, automated deployments, automated hyper-parameter tuning, and more will be discussed.

This course assumes the data scientist has an understanding of machine learning and common terminology and integration in machine learning projects. The course also assumes the data scientist has knowledge of Azure and the Azure portal.

Authoring for Pluralsight – Developing Microsoft Azure Intelligent Edge Solutions

Off to start another course for Pluralsight. This time its Developing Microsoft Azure Intelligent Edge Solutions. If you would like to check out any of my other courses, visit my author’s profile. The new course will cover the following topics:

Edge
- IoT Architecture
- IoT use cases and solutions
- Edge Architecture
Azure IoT Hub
- Overview of the IoT Ecosystem in Azure
- IoT Hub message routing
- Stream processing overview
Hot, Warm, and Cold paths
- Use cases for hot, warm, and cold paths
- Hot path with event hubs and log app
- Warm path with Cosmos DB
- Cold path with Azure Blob Storage
Real Time and Batch Processing
- Overview and Demos of Stream Analytics Service
- Overview and Demos of Time Series Insights

Azure IoT Edge – YOLO, Stream Analytics Service, and Blob Storage

As a continuation of the Izon camera hack //TODO: link to previous article, I wanted to detect if my dog was using the doggy door in the main room. The approach was going to be simple at first, detect the dog in the room and not in the room. When in the room changes (or not in the room), upload 10 seconds worth of images to Azure to see if the dog used the door.

Image Classification

To detect the dog, the first and largest challenge to these types of tasks is getting enough images to train the model. For me, this meant saving images of the dog in a pre-aligned shot. This is easy enough to accomplish; the room the images will be processed in should only have the dog moving in it. Since he is the only moving object, YOLO can be used to detect the position of objects in the room and then the position of these objects can be checked to see if there is any movement. If there is any movement, the images can be saved for later categorization. To accomplish this, there will be four modules:

Camera Module – Accesses the camera feeds to save the images
Object Detection Module – Uses YOLO to detect object and object positions
Motion Detection Module – Uses Stream Analytics Service to detect if object positions are moving.
Image Storage Module – Uses Blob Storage so save and delete the images

The Camera module will send the timestamped images to the Object Detection Module and the Image Storage Module. The Object Detection Module will then use YOLO to detect the objects and their positions in the image. Those detection results will be sent to the Motion Detection Module, which will use Streaming Analytics Service to see if there was motion detected over the last ten seconds. If there is no motion detected over the last ten seconds, then the Motion Detection Module will send a delete command to the Image Storage Module to remove the image without motion from the store. The routing Table will look as so:

	"routes": {
	"cameraToObjectDetection": "FROM /messages/modules/camera/outputs/imageOutput INTO BrokeredEndpoint(\"/modules/objectDetection/inputs/incomingImages\")",
	"cameraToImageStorage": "FROM /messages/modules/camera/outputs/imageOutput INTO BrokeredEndpoint(\"/modules/imageStorage/inputs/incomingImages\")",
	"objectDetectionToMotionDetection": "FROM /messages/modules/objectDetection/outputs/objectDetectionOutput INTO BrokeredEndpoint(\"/modules/motionDetection/inputs/incomingObjectDetection\")",
	"motionDetectionToDeleteImage": "FROM /messages/modules/motionDetection/outputs/motionDetectionOutput INTO BrokeredEndpoint(\"/modules/imageStorage/inputs/deleteImages\")"
	}

view raw

routes.json

hosted with ❤ by GitHub

These modules will be broken up into their own articles for readability and searchability. If there is no link to a module article it is because that article is not completed or is not published yet.