Publishing and consuming custom vision models in Azure involves a streamlined process that enables developers to deploy trained models and integrate them into applications. After training a Custom Vision model using the Azure Custom Vision service, you need to publish it to make predictions availab…Publishing and consuming custom vision models in Azure involves a streamlined process that enables developers to deploy trained models and integrate them into applications. After training a Custom Vision model using the Azure Custom Vision service, you need to publish it to make predictions available through an API endpoint.
To publish your model, navigate to the Custom Vision portal and select your trained iteration. Click the Publish button and specify a prediction resource where the model will be hosted. You must provide a name for the published iteration, which becomes part of your API endpoint. The publishing process deploys your model to Azure infrastructure, making it accessible for real-time predictions.
Once published, you receive two key pieces of information: the Prediction URL and Prediction Key. The Prediction URL serves as the endpoint where you send images for classification or object detection. The Prediction Key authenticates your requests to the service.
Consuming the model involves making HTTP POST requests to the prediction endpoint. You can send images either as binary data or via URL. The request headers must include the Prediction-Key for authentication and appropriate Content-Type specifications. The service returns JSON responses containing predictions with probability scores and tag names for classification, or bounding box coordinates for object detection scenarios.
Azure provides SDKs for multiple programming languages including Python, C#, and JavaScript, simplifying integration into applications. These SDKs handle authentication and request formatting, allowing developers to focus on application logic rather than API mechanics.
For production environments, consider implementing caching strategies, error handling, and retry logic. Monitor your prediction resource usage through Azure metrics to manage costs and performance. You can also export trained models to run on edge devices using formats like TensorFlow, CoreML, or ONNX, enabling offline predictions in scenarios where cloud connectivity is limited or latency is critical.
Publishing and Consuming Custom Vision Models
Why Is This Important?
Publishing and consuming Custom Vision models is a critical skill for Azure AI engineers because it bridges the gap between model training and real-world application deployment. Understanding this process enables you to make trained models available for production use, integrate computer vision capabilities into applications, and manage model versions effectively. For the AI-102 exam, this topic tests your ability to deploy and operationalize AI solutions.
What Is Publishing and Consuming Custom Vision Models?
Custom Vision is an Azure Cognitive Service that allows you to build, train, and deploy custom image classification and object detection models. Publishing refers to making a trained iteration of your model available through a prediction endpoint. Consuming refers to calling that endpoint from applications to get predictions on new images.
Key concepts include: - Iterations: Each time you train a model, a new iteration is created - Publishing: Deploying a specific iteration to a prediction resource - Prediction Endpoint: The URL used to submit images for classification or detection - Prediction Key: Authentication key required to access the endpoint
How It Works
Step 1: Train Your Model After uploading and tagging images, train your model to create an iteration.
Step 2: Publish the Iteration Select a trained iteration and publish it with a name. You must specify the prediction resource where it will be deployed.
Step 3: Get Prediction Credentials Obtain the prediction endpoint URL, prediction key, and project ID from the Custom Vision portal or via API.
Step 4: Consume the Model Make REST API calls or use the SDK to send images to the prediction endpoint. The response includes predicted tags with confidence scores.
Code Example (REST API call):
POST https://{endpoint}/customvision/v3.0/Prediction/{projectId}/classify/iterations/{publishedName}/image Headers: Prediction-Key: {your-key}Body: Binary image data
SDK Methods: - ClassifyImage() - for classification with image file - ClassifyImageUrl() - for classification with image URL - DetectImage() - for object detection with image file - DetectImageUrl() - for object detection with image URL
Exam Tips: Answering Questions on Publishing and Consuming Custom Vision Models
1. Know the difference between training and prediction resources: Training resources are used to build models; prediction resources host published iterations for inference.
2. Remember the publishing requirements: You must specify a publication name and a prediction resource when publishing an iteration.
3. Understand endpoint URLs: Classification uses /classify/ in the path, while object detection uses /detect/. Questions may test whether you can identify the correct endpoint.
4. Authentication: The Prediction-Key header is required for all prediction API calls. This is different from the training key.
5. Response format: Classification returns tags with probabilities; object detection returns bounding boxes with tags and probabilities.
6. Compact vs. Standard domains: Compact domains allow model export for offline use; Standard domains require cloud-based prediction. Know when to use each.
7. SDK vs REST: Be familiar with both approaches. SDK methods like ClassifyImageUrl() abstract the REST calls but accomplish the same result.
8. Iteration management: Only published iterations can receive predictions. Unpublishing an iteration removes it from the prediction endpoint but does not delete it.
9. Watch for scenario-based questions: These may describe an application requirement and ask you to select the correct API method or endpoint configuration.
10. Performance considerations: The prediction endpoint has rate limits. For high-volume scenarios, consider exporting compact models or scaling prediction resources.