Building custom vision models code-first in Azure involves using the Custom Vision SDK and REST APIs to programmatically create, train, and deploy image classification or object detection models. This approach offers greater flexibility and automation compared to using the Custom Vision portal.
To…Building custom vision models code-first in Azure involves using the Custom Vision SDK and REST APIs to programmatically create, train, and deploy image classification or object detection models. This approach offers greater flexibility and automation compared to using the Custom Vision portal.
To begin, you need to set up your Azure Custom Vision resources - both a training resource and a prediction resource. Install the Azure Cognitive Services Custom Vision SDK using pip: 'pip install azure-cognitiveservices-vision-customvision'.
The code-first workflow starts by importing necessary libraries and authenticating using your endpoint and training key. Create a CustomVisionTrainingClient object to interact with the service. You can then create a new project specifying the project name, domain type (General, Food, Landmarks, etc.), and classification type (Multiclass or Multilabel).
Next, create tags that represent your classification categories using the create_tag() method. Upload training images associated with each tag using create_images_from_files() or create_images_from_urls(). Azure recommends at least 50 images per tag for optimal results.
Once images are uploaded, call the train_project() method to initiate model training. This returns an iteration object that you can monitor for completion status. Training typically takes several minutes depending on dataset size.
After training completes successfully, publish the iteration to make it available for predictions using publish_iteration(). This requires specifying a prediction resource ID where the model will be deployed.
For making predictions, create a CustomVisionPredictionClient using your prediction endpoint and key. Use classify_image() for local images or classify_image_url() for web-hosted images. The response contains probability scores for each tag.
Code-first approaches enable CI/CD integration, batch processing of training data, automated retraining pipelines, and programmatic model management - essential capabilities for production machine learning workflows in enterprise environments.
Building Custom Vision Models Code First
Why It Is Important
Building custom vision models using a code-first approach is essential for enterprise-grade AI solutions. It enables automation, version control, and integration into CI/CD pipelines. For the AI-102 exam, Microsoft emphasizes programmatic approaches because they reflect real-world scenarios where scalability and repeatability are critical. Understanding the code-first method demonstrates proficiency in implementing production-ready computer vision solutions.
What It Is
The code-first approach to Custom Vision involves using SDKs and REST APIs to create, train, and deploy image classification and object detection models programmatically. Instead of using the Custom Vision portal, developers write code using:
• Custom Vision Training SDK - For creating projects, uploading images, tagging, and training models • Custom Vision Prediction SDK - For making predictions against trained models • REST APIs - For programmatic access to all Custom Vision functionality
How It Works
Step 1: Set Up Resources Create Custom Vision training and prediction resources in Azure. Note the endpoint URLs and keys for both resources.
Step 2: Create a Project Use the Training SDK to create a project specifying the domain type (General, Food, Landmarks, Retail, etc.) and project type (Classification or Object Detection).
Step 3: Upload and Tag Images Programmatically upload training images and associate them with tags. For classification, assign tags to entire images. For object detection, specify bounding box regions.
Step 4: Train the Model Call the training method and wait for training to complete. You can choose between Quick Training and Advanced Training options.
Step 5: Publish the Iteration Publish the trained iteration to a prediction endpoint, specifying the prediction resource ID.
Step 6: Make Predictions Use the Prediction SDK to classify new images or detect objects, providing the project ID, published iteration name, and image data.
Key Code Concepts
• CustomVisionTrainingClient - Authenticates and manages training operations • CustomVisionPredictionClient - Handles prediction requests • create_project() - Creates a new Custom Vision project • create_images_from_files() - Uploads batched images with tags • train_project() - Initiates model training • publish_iteration() - Makes the model available for predictions • classify_image() or detect_image() - Gets predictions
Exam Tips: Answering Questions on Building Custom Vision Models Code First
1. Know the Two Resources: Remember that Custom Vision requires separate training and prediction resources. Questions often test whether you understand which endpoint and key to use for which operation.
2. Understand Domain Selection: Compact domains are optimized for edge deployment and export. General domains provide better accuracy but larger model sizes. Exam questions may ask which domain suits specific scenarios.
3. Remember the Workflow Order: Create project → Upload images → Create tags → Train → Publish → Predict. Questions may present steps in wrong order.
4. Iteration Management: Models must be published before predictions can be made. The iteration name is required when calling prediction methods.
5. Minimum Image Requirements: Training requires at least 5 images per tag for classification and 15 images per tag for object detection. This is frequently tested.
6. SDK vs REST: Know that both achieve the same results. REST APIs require manual HTTP requests while SDKs provide convenient wrapper methods.
7. Export Capabilities: Only compact domains support model export to formats like TensorFlow, CoreML, ONNX, or Docker containers.
8. Authentication Pattern: Training and prediction clients use ApiKeyCredentials with endpoint and key parameters. Watch for questions mixing up authentication methods.