Implement custom translation, including training, improving, and publishing a custom model

To implement custom translation in a Microsoft Azure AI solution, you can leverage Azure Cognitive Services Translator Text API. This API allows you to train, improve, and publish a custom translation model to meet your specific requirements. In this article, we will explore how to design and implement a custom translation solution using Azure AI.

Prerequisites

Before diving into the implementation, ensure you have the following prerequisites:

1. Azure subscription: If you don’t have one, create a free account here.
2. Azure Cognitive Services account: Create a Translator Text API resource in your Azure subscription.

Step 1: Create a Translator Text API resource

1. Login to Azure portal.
2. Click on “Create a resource” and search for “Translator Text API”.
3. Select the Translator Text API from the search results.
4. Click on “Create” to create a new Translator Text API resource.
5. Provide a unique name, choose the subscription, resource group, and pricing tier for the resource.
6. Review the terms and conditions, then click on “Create” to create the resource.

Step 2: Obtain the access key and endpoint

After the creation of the Translator Text API resource, follow these steps to obtain the required access key and endpoint:

1. Open the newly created Translator Text API resource in the Azure portal.
2. In the left-hand menu, click on “Keys and Endpoint”.
3. Copy either “Key1” or “Key2” and make a note of it. This will be your access key.
4. Copy the “Endpoint” value and make a note of it as well.

Step 3: Implement translation using Translator Text API

To implement translation, you can utilize the Translator Text API provided by Azure Cognitive Services. Here is an example of how to use the API in Python:

import requests
import json

# Usage example
text_to_translate = 'Hello, how are you?'
translated_text = translate_text(text_to_translate, 'fr')
print(f'Translated: {translated_text}')

Ensure to replace 'YOUR_TRANSLATOR_TEXT_API_KEY', 'YOUR_TRANSLATOR_TEXT_API_ENDPOINT', and 'YOUR_TRANSLATOR_TEXT_API_REGION' with the appropriate values from the Translator Text API resource you created earlier.

In the example above, we define a translate_text function that takes the text to translate and the target language as input parameters. The function sends a POST request to the Translator Text API endpoint with the necessary headers and body containing the text to translate. The translated text is then extracted from the response and returned.

Step 4: Train and improve the translation model

To create a custom translation model, you need to train and improve the existing models provided by the Translator Text API. This can be achieved by providing your custom translation examples to enhance the translation quality. Follow these steps to train and improve the translation model:

1. Prepare a translation document that contains examples of translations specific to your domain or requirements.
2. Upload the translation document to the Translator Text API using the Add Resource method. Specify the source and target language along with the uploaded document.
3. Use the Translator Text API to send translation requests for your custom scenarios.
4. Provide feedback to Microsoft on translation quality to help improve the underlying models.

By iteratively training and improving the translation model, you can enhance the accuracy and relevance of translations for your specific use cases.

Step 5: Publish and use the custom translation model

After training and improving the translation model, you can publish it to make it available for use in your applications. Follow these steps to publish the custom translation model:

1. Navigate to your Azure Cognitive Services resource in the Azure portal.
2. In the left-hand menu, click on “Cognitive Services” and then select “Translator Text API”.
3. Under the “Intelligence” section, click on the “Customization” tab.
4. Select the trained translation model you want to publish and click on “Publish” to make it available for consumption.
5. Once the model is published, you will receive an API key and endpoint specific to your custom translation model.
6. Use the obtained API key and endpoint in your application code to utilize the custom translation model.

Publishing the custom translation model allows you to take advantage of the trained model’s accuracy and domain-specific translations, tailored to your specific needs.

Conclusion

Implementing custom translation in a Microsoft Azure AI solution involves creating a Translator Text API resource, obtaining the access key and endpoint, and utilizing the Translator Text API to translate text. By training, improving, and publishing a custom translation model, you can achieve higher accuracy and relevance in translations that align with your specific requirements. Leverage Azure Cognitive Services to build powerful language translation capabilities into your applications.