Import and export language understanding models

Step 1: Prepare the Model

First, ensure that your language understanding model is prepared for import. Microsoft provides a Python library called azureml-contrib-interpret to help with model preparation. You can use this library to convert your model into an Open Neural Network Exchange (ONNX)-compliant format.

Step 2: Create and Configure an Azure Machine Learning Workspace

Next, you need to create an Azure Machine Learning workspace to manage your models. You can do this through the Azure portal or by using the Azure CLI. Once you have set up the workspace, make sure to configure it with the necessary dependencies, such as Azure Blob Storage.

Step 3: Register the Model

To import your language understanding model, you must register it with Azure. This step involves creating a model object and specifying its details, such as the model file, framework, and dependencies. You can use the azureml.core.Model class in Python to accomplish this.

Step 4: Package and Upload the Model

After registering the model, package it into a Docker container that can be deployed in Azure. Use Azure Machine Learning’s built-in support for Docker containers, or create a custom container if required. Once packaged, upload the container to Azure Container Registry or another container registry service.

Step 5: Deploy the Model

Deploying the model involves creating an Azure Container Instance or Azure Kubernetes Service to host the containerized model. Azure Machine Learning provides tools and APIs to facilitate this process. Once deployed, you can use the model for various language understanding tasks.

Exporting a Language Understanding Model:

Exporting a language understanding model allows you to share or deploy it outside of Azure. Here are the steps to export a model:

Step 1: Retrieve the Model

To export a language understanding model, you first need to retrieve it from Azure. This involves accessing the registered model object in your Azure Machine Learning workspace.

Step 2: Package the Model

Before exporting the model, package it into a format suitable for the target environment. If the model is in ONNX format, you can skip this step. Otherwise, you may need to convert the model to a compatible format using libraries such as ONNX Runtime.

Step 3: Export the Model

Exporting the model involves saving it to a file or creating an archive for distribution. Choose an appropriate file format based on your requirements and the target environment. Common formats include ONNX, TensorFlow, PyTorch, and Keras.

Step 4: Share or Deploy the Model

Once exported, you can share the model with others or deploy it to a different platform or infrastructure. This process may vary depending on the target environment. Follow the documentation of the specific platform or framework for deployment instructions.

Conclusion:

Importing and exporting language understanding models in Azure is crucial for building effective AI solutions. Whether you need to bring a pre-trained model into Azure or distribute your models to other platforms, Azure Machine Learning provides the necessary tools and workflows. By following the steps outlined in this article, you can seamlessly import and export language understanding models to enhance your AI capabilities.