Training and publishing custom document models in Azure AI Document Intelligence allows you to create specialized models tailored to your specific document types and business needs. This process involves several key steps that enable accurate extraction of information from your unique documents.
F…Training and publishing custom document models in Azure AI Document Intelligence allows you to create specialized models tailored to your specific document types and business needs. This process involves several key steps that enable accurate extraction of information from your unique documents.
First, you need to gather a representative sample of your documents. Azure recommends at least five sample documents of the same type, though more samples typically improve model accuracy. These documents should represent the variety of layouts and content you expect to process.
Next, you create a project in Document Intelligence Studio or use the REST API/SDKs. Within this project, you label your documents by identifying and tagging the fields you want to extract. For example, in an invoice, you might label vendor name, invoice date, total amount, and line items. The labeling process teaches the model what information matters for your use case.
Once labeling is complete, you train the model using Azure's machine learning capabilities. The training process analyzes the labeled documents and learns patterns to recognize similar fields in new documents. Training typically takes a few minutes depending on document complexity and quantity.
After training, you should evaluate the model's performance using the confidence scores and accuracy metrics provided. If results are unsatisfactory, you can add more labeled samples and retrain to improve accuracy.
When satisfied with performance, you publish the model to make it available for production use. Publishing creates an endpoint that applications can call to analyze documents. You can integrate this endpoint into your workflows using REST APIs, SDKs for various programming languages, or Azure Logic Apps.
Custom models can be composed into larger models, allowing you to handle multiple document types with a single API call. This approach provides flexibility while maintaining accuracy across diverse document processing scenarios in your knowledge mining solutions.
Training and Publishing Custom Document Models in Azure AI Document Intelligence
Why is This Important?
Training and publishing custom document models is a critical skill for Azure AI Engineers because organizations often deal with unique document formats that pre-built models cannot handle effectively. Custom models allow you to extract specific data from invoices, contracts, forms, and other business documents tailored to your organization's needs. This capability is essential for automating document processing workflows and reducing manual data entry.
What Are Custom Document Models?
Custom document models in Azure AI Document Intelligence are machine learning models trained on your specific document types. There are two main types:
Custom Template Models: Best for documents with consistent visual layouts where fields appear in the same location. These require fewer training samples (minimum 5 documents).
Custom Neural Models: Handle documents with varying structures and formats. They use deep learning and require more training data but offer greater flexibility.
How Training Works
1. Create a Document Intelligence Resource in Azure portal 2. Prepare Training Data: Collect sample documents (PDF, JPEG, PNG, BMP, or TIFF format) 3. Label Your Documents: Use Document Intelligence Studio to identify and tag the fields you want to extract 4. Configure Storage: Upload documents to Azure Blob Storage with appropriate CORS settings 5. Train the Model: Initiate training through the Studio, REST API, or SDK 6. Evaluate Results: Review confidence scores and accuracy metrics
How Publishing Works
After training, you can:
- Test the model using Document Intelligence Studio - Deploy the model by using its model ID in API calls - Create composed models by combining multiple custom models (up to 200 models) - Version your models to maintain different iterations
Key Configuration Requirements
- Storage account must have CORS enabled for Document Intelligence Studio access - Training documents should be high quality and representative of production documents - Minimum 5 labeled documents for template models, 10+ recommended for neural models - Document size limit is 500 MB for standard tier
Exam Tips: Answering Questions on Training and Publishing Custom Document Models
Tip 1: Remember the minimum document requirements - 5 for template models is a frequently tested concept.
Tip 2: Understand when to use template vs neural models. Template models are for fixed layouts; neural models handle variable structures.
Tip 3: Know that composed models combine multiple custom models and can classify which sub-model to use based on document type.
Tip 4: CORS configuration on blob storage is essential for Document Intelligence Studio - expect questions about troubleshooting access issues.
Tip 5: Be familiar with confidence scores and how they indicate model reliability. Low scores suggest more training data or better labeling is needed.
Tip 6: Remember that custom models are accessed via their model ID when making API calls after training.
Tip 7: Understand the difference between the free tier (500 pages/month) and standard tier capabilities for production scenarios.
Tip 8: Questions may present scenarios asking you to choose between pre-built and custom models - custom models are appropriate when pre-built models do not support your specific document structure.