Export a model to run on a specific target

Microsoft Azure provides a powerful platform for developing and deploying AI models. Once you have trained and fine-tuned your model, the next step is to export it so that it can be deployed and run on a specific target, such as a local machine, a server, or an edge device. In this article, we’ll explore the different options and techniques available to export a model in Azure.

Exporting a Model as a TensorFlow SavedModel

One of the most commonly used formats for exporting AI models is TensorFlow’s SavedModel format. Azure provides built-in support for exporting models in this format. To export your model as a TensorFlow SavedModel, you can use the following code snippet:

import tensorflow as tf
from tensorflow import keras

model = keras.models.load_model("path/to/your/model.h5")
tf.saved_model.save(model, "path/to/exported/model")

This code snippet assumes that you have already trained and saved your model using the Keras library in TensorFlow. Replace the placeholders with the actual paths to your model.

Exporting a Model as an ONNX Model

Open Neural Network Exchange (ONNX) is an open format for representing machine learning and deep learning models. It allows interoperability between different frameworks, making it easier to deploy models on various platforms. Azure provides support for exporting models in the ONNX format. To export your model as an ONNX model, you can use the following code snippet:

import onnxmltools
from onnxmltools import convert

model = keras.models.load_model("path/to/your/model.h5")
onnx_model = convert.from_keras(model)
onnxmltools.utils.save_model(onnx_model, "path/to/exported/model.onnx")

This code snippet assumes that you have the onnxmltools library installed. Replace the placeholders with the actual paths to your model.

Exporting a Model as a Docker Container

Docker containers provide a lightweight and portable way to package and deploy applications, including AI models. Azure provides support for exporting models as Docker containers, allowing you to easily deploy them on various platforms. To export your model as a Docker container, you can use the Azure Machine Learning service. Follow the steps below:

1. Create an Azure Machine Learning workspace.
2. Register your model in the workspace.
3. Define an inference configuration that specifies the dependencies and runtime environment for your model.
4. Define a deployment configuration to specify the target platform and resources.
5. Deploy the model as a Docker container.

The process of exporting a model as a Docker container involves several steps and configurations. For detailed instructions, refer to the Microsoft Azure documentation on how to deploy models as Docker containers using Azure Machine Learning service.

Exporting a Model for Deployment on Azure IoT Edge

Azure IoT Edge allows you to deploy cloud workloads, including AI models, to edge devices such as Raspberry Pi, Jetson Nano, or Windows IoT devices. To export your model for deployment on Azure IoT Edge, you can use Azure Machine Learning service. Follow the steps below:

1. Create an Azure Machine Learning workspace.
2. Register your model in the workspace.
3. Define an inference configuration that specifies the dependencies and runtime environment for your model.
4. Define a deployment configuration that specifies the target device and resources.
5. Deploy the model to Azure IoT Edge.

The process of exporting a model for deployment on Azure IoT Edge involves similar steps as exporting it as a Docker container. However, the deployment configuration is specific to Azure IoT Edge. For detailed instructions, refer to the Microsoft Azure documentation on how to deploy models to Azure IoT Edge using Azure Machine Learning service.

In this article, we explored different techniques to export a model for running on a specific target in the context of designing and implementing a Microsoft Azure AI solution. We discussed exporting models as TensorFlow SavedModels, ONNX models, Docker containers, and for deployment on Azure IoT Edge.

Depending on your specific use case and requirements, you can choose the appropriate method to export your model and leverage the power of Azure to deploy and run your AI solution seamlessly.