Session Title: Creating applications that can see, hear, speak or understand - using Microsoft Cognitive Services
Session Abstract: In this workshop you will be introduced to the Microsoft Azure Cognitive Services, a range of offerings you can use to infuse intelligence and machine learning into your applications without needing to build the code from scratch. We will cover pre-trained AI APIs, such as computer vision and text analytics, that are accessed by REST protocol. Next we will dive into Custom AI that uses transfer learning - Microsoft Azure Custom Vision. This enables you to provide a small amount of your own data to train an image classification model. Wrapping the workshop up by building our custom trained AI into an application - using Logic Apps, this technology is ideal for building data pipeline processes that work with your machine learning models.
- Clone this repository to your local machine to gain images and code samples you need for the demos:
git clone https://github.com/amynic/AINights.gitor choose 'Clone or Download' green button and then 'Download ZIP' - Microsoft Azure Subscription
- Laptop with a modern web browser (Google Chrome, Microsoft Edge)
All demos and content have been tested on a Windows PC, however all options should run from macOS and Linux machines as well. Please provide information via an issue or pull request if you have feedback on other operating systems
- Task 1: Microsoft Azure Cognitive Services - Custom Vision Go to Section
- Task 2: Build Custom AI into an Application - Azure Logic Apps Go to Section
If you find this workshop useful, find further Bonus Content HERE!
Using Microsoft Azure Custom Vision service you can start to build your own personalised image classification and object detection algorithms with very little code. In this exercise we will create a dog-breed classification algorithm using Dog images from the ImageNet open dataset created by Standford University
We have 7 Classes of dogs each with 30 images (available in a .zip file here)
- Beagle
- Bernese Mountain Dog
- Chihuahua
- Eskimo Dog (aka Husky)
- German Shepherd
- Golden Retriever
- Maltese
There is also a set of test images (not for training) in this .zip folder.
First create a Custom Vision instance in your Azure account.
- Go to the Azure Portal main dashboard.
- Click 'Create a Resource' in the top left
- Search for 'Custom Vision'
- On the description pane for Custom Vision click Create.
- Enter details to create
- A name for the service
- Select your subscription
- Select the data centre location (in this example South Central US, but you can select your own region)
- Choose the S0 tier for both 'Prediction pricing tier' and Training pricing tier
- Select or create a resource group and make sure it is in the same data centre location (in this case 'ainights' in South Central US)
- Click Create
Now we can build our classifier, navigate to https://www.customvision.ai and choose sign in. Sign in with your Azure credentials account
Accept the terms and conditions box to continue
Once loaded choose 'New Project' which opens a window to enter details
- Name: choose a suitable name
- Description: add a description of the classifier (example shown in image below)
- Resource Group: choose the resource group you created your custom vision service in (example: ainights[SO])
- Project Types: Classification
- Classification Types: Multiclass (Single tag per image)
- Domains: General
Choose 'Create Project' and you will land on an empty workspace like below
Now we can start adding images and assigning them tags to create our image classifier
In the top left, select 'Add images', browse for the first folder of images from the .zip folder - Beagle - and select all 30 of the images in the folder.
Add the tag 'beagle' to the Beagle dog images and select 'Upload 30 files'
Once successful you receive a confirmation message and you should see your images are now available in the workspace
Now complete the same steps of uploading and tagging images for the other 6 dog categories in the folder. For each type of dog:
- Click add images
- Select the 30 new dog images
- Add the class label (beagle, german-shepherd, maltese etc)
- choose upload
- confirm images uploaded into the workspace
Now you should have all categories uploaded and on the left hand side you can see your dog classes and you can filter depending on type of dog image
Now you are ready to train your algorithm on the dog image data you have uploaded. Select the green 'Train' button in the top right corner. For this demo, you can use the "Fast Training" option.
Once the training process is complete it will take you to the Performance tab. Here you will receive machine learning evaluation metrics for your model
Now we have a model we need to test the model. Choose the 'Quick Test' button in the top right (next to the train button) this will open a window where you can browse for a local image or enter a web URL.
Browse for an image in the test folder (images the model have not been trained on) and upload. The image will be analysed and a result returned of what dog the model thinks it is (prediction tag) and the models confidence of its result (prediction probability)
Repeat this process for other images in the test folder to see how the model performs
If you click on the 'Predictions' tab on the top toolbar - you should see all the test images you have submitted. This section is for re-training, as you get new data you can add this to your model to improve its performance. The images are ordered by importance - the image, which if classified correctly, will add the most new information to the model is listed first. Whereas the last image might be very similar to other images already learnt by the model so this is less important to classify correctly.
To add these images to the model - select the first image, review the results the model provided and then in the 'My Tags' box enter the correct tag and click 'save and close'
This image will disappear from the your predictions workspace and be added to the training images workspace. Once you add a few new images and tags you can re-train the model to see if there are improvements.
To use this model within applications you need the prediction details. Therefore, you have to go to the Performance tab from the top bar, click the Publish button and provide a name for this published iteration.
You can now select the Prediction URL button to gain all information you need to create a Postman call to your API, by setting the URL, the Header and the Body (using both an image or an image URL). You must add the "Prediction-Key" and "Content-Type" items to header variables in Postman.
Great work! you have created your specialised dog classification model using the Azure Custom Vision Service
In this section you will build an Azure Logic App to consume your Custom Vision AI dog classification application
First we need to create two Azure Storage Accounts.
Go to the azure portal and click create new resource in the top left corner. Select the section Storage and choose the first option Storage Account.
We are going to create two storage accounts:
- one for the images to be dropped into to be processed (called ainightstor)
- another for the results after processing to be uploaded to (called resultainights)
Complete the process below twice so you have two storage accounts in total
On the storage account creation page enter options to setup your storage account:s
- Subscription: choose your subscription
- Resource Group: choose the resource group you have been using for this workshop (e.g. ainights)
- Storage Account Name: (must be unique) enter an all lowercase storage account name. Such as ainightsstor(yourname) or resultsainights(yourname) - append your name to the end of the storage account name so you know its unique (remove the brackets)
- Location: your closest data center
- Performance: Standard
- Account Kind: Blob Storage
- Replication: Locally-redundant storage (LRS)
- Access Tier: Hot
Select Review + create, confirm validation is passed and then select Create
Once your deployment is complete, got to the resource and review the account settings. Select Blobs to review your empty blob storage account.
We need to add a container to the storage account to store our images and results.
Select the + Container button and create a name for the container
an example for the ainightsstor account would be images
an example for the resultsainights account would be results
For the public access level setting select Container (anonymous read access for containers and blobs)
Complete the above for an image storage account and a results storage account with the same settings
Now we will create a Logic App - this will connect your image storage account to your AI classification service and put the results in your results storage account
Head to the Azure Portal Homepage. We are going to use Event Grid, a service that detects triggers in an Azure subscription (in our case, when a new blob is created in your Azure Storage account). Before we build with this - we must register it.
Go to subscriptions in the left panel, select your subscription and find Resource Providers in the left pane. If it's not in left panel, selct "All services" and find in here. Once the resource providers are listed - search "event" and select Microsoft.EventGrid.
If this is not already status registered, select register from the toolbar
Once registered with a green tick - go back to the Azure Portal Homepage. Select Create a Resource. Type Logic App and select the service
Create the logic app by entering some setup detail like below:
- Name: suitable name for the dog classification application
- Subscription: Choose your subscription
- Resource Group: (use existing e.g. ainights) select the resource group you have been working for the whole workshop
- Location: choose the data center closest to you
- Log Analytics: off
Choose Create
Once created, go to resource. From here we can create our logic process. Select Logic app designer from the left menu and then the When an Event Grid resource event occurs option
Connect to Azure event grid by signing in using your Azure credentials
Once connected and you see a green tick, select continue.
Select the options below:
- Subscription: your subscription
- Resource Type: Microsoft.Storage.StorageAccounts
- Resource Name: choose your image storage account (e.g. ainightsstor)
- Event Type Item - 1: Microsoft.Storage.BlobCreated
Then choose next step. Type Parse JSON and select the parse JSON operator as part of the data Data Operations category
- Content: select the box and from the Dynamic Content box on the right, select Body
- Schema: select this box and enter the JSON schema provided in the logic-app-schema1 file
Then choose next step. Type custom vision and select the Predict tags from image URL (preview) as below
Now we need to fill in the details of the custom vision process
- Project ID: Find the project ID from the settings logo in the top right of the Custom Vision webpage
- Published Name: You can find the published name from the performance tab in the Custom Vision service
-
- Image URL: select the input box and on the right side select URL from Parse JSON outputs
Choose next step
type for each and select the grey control step called for each Once selected in the output from previous step box, select the box and from Dynamic content select predictions from the Parse JSON 2 category
Choose Add an action
Search Control, select the control icon and then from the results, select Condition
In the Condition box, select choose a value. From Dynamic content find 'Predict Tags from Image URL' and then Probability
Set the condition to be Predicition Probability greater than 0.7 (as shown below)
In the If True box select Add an action
Search for Azure Blob Storage and select the icon for Create Blob
In connection name enter results and select your results blob storage account name from the listed options and select create
In folder path, select the folder icon, far right, and choose the container name you created that is populated
Select the Blob name field and enter: result-(then from the Dynamic content box under Parse Json (1) select id)
Under Blob Content, select the field and in the Dynamic Content box on the right, select see more under the Parse Json 2 section. Then select tagName, enter a colon ":" and then select probability
Finally save the logic app in the top action bar
Once saved, lets test the app for the desired outcome. Select Run from the top action bar
Now navigate to your images storage account (easy to find from the resource group section). Choose Blob and select the images container. In there you should see an upload button. Upload one of the images from the Dogs data testset folder
Once uploaded, navigate back to your Logic App main page and review the runs history section at the bottom of the page. Select the successful run and review the inputs and outputs from the
All sections should have a green tick and you can select each one to view the input and output between the layers (this is also a great way to debug if it doesn't run as expected)
Finally navigate to your results blob storage account, select blob, enter the results container and review the file now created there. The contents of the file should show similar to the below - given the dog image input, the predicted class of the dog and also a confidence score
If you have enjoyed this lab session and wish to learn more about the Azure Cognitive Services, check out the bonus-content page for:
Find all this content HERE!
Finally, If you don't expect to need these resources in the future, you can delete them by deleting the resource group. To do so, select the resource group for this workshop, select Delete, then confirm the name of the resource group to delete.