Use automated machine learning for computer vision

Imagine being able to build a powerful computer vision solution without spending hours on manually training and fine-tuning models. With automated machine learning (AutoML), this is now a reality. In this article, we will explore how to leverage AutoML for computer vision tasks, using Microsoft Azure as our platform.

Azure Machine Learning for Data Science

Azure provides a comprehensive set of tools and services to design and implement data science solutions. With AutoML, we can automate the process of selecting and tuning machine learning models, enabling us to focus on problem definition and data preparation.

Understanding Computer Vision

Before diving into the implementation, let’s have a brief overview of computer vision and its applications. Computer vision involves training models to interpret and understand visual data, such as images and videos. It finds application in various domains, including object detection, image classification, facial recognition, and more.

Step 1: Create a new resource group in Azure

Begin by creating a new resource group in Azure. A resource group is a logical container for resources like virtual machines, storage accounts, and machine learning workspaces. It helps organize and manage resources in a cohesive manner.

Step 2: Set up an Azure Machine Learning workspace

Next, create an Azure Machine Learning workspace within the resource group. The workspace acts as a centralized hub for all your machine learning experiments, datasets, and models. It provides a collaborative environment for data scientists and engineers to work together.

Step 3: Prepare your data

Before training a model, you need to prepare your data. Azure provides various tools to preprocess and augment your images. You can resize images, apply transformations, and even generate synthetic data to improve model performance.

Step 4: Create an AutoML experiment

Now it’s time to create an AutoML experiment. An experiment encapsulates the entire pipeline of training and evaluating models. With AutoML, you don’t need to worry about selecting an appropriate algorithm or tuning hyperparameters. The service takes care of these steps for you.

Step 5: Configure AutoML settings

Configure the settings for your AutoML experiment. Specify the type of task you want to perform, such as image classification or object detection. You can also define the compute target, which is the infrastructure used to train your models. Azure supports various compute targets, including local machines, remote servers, and GPU clusters.

Step 6: Run the AutoML experiment

Once the settings are configured, kick off the AutoML experiment. Azure will automatically explore and evaluate a range of models, using different algorithms and hyperparameters. It will then select the best-performing model based on a specified metric, such as accuracy or precision.

Step 7: Deploy and consume the model

After the experiment is complete, you can deploy and consume the trained model. Azure provides options to deploy your model as a web service, making it easy to integrate into your applications. You can also leverage Azure’s scalable and reliable infrastructure to serve predictions efficiently.

By following these steps, you can leverage AutoML to build robust computer vision solutions with ease. With the power of Azure, you can focus on your problem domain, while the platform handles the complexities of model selection and tuning.

Let’s take a closer look at some code snippets to better understand the implementation details. Note that the code examples provided here are simplified for brevity. For a complete code walkthrough, refer to the official Azure Machine Learning documentation.

Code Snippet 1: Creating an AutoML experiment

import azureml.core
from azureml.train.automl import AutoMLConfig

Code Snippet 2: Deploying the trained model

from azureml.core.webservice import AciWebservice
from azureml.core.model import InferenceConfig

service.wait_for_deployment(show_output=True)

In Code Snippet 1, we create an AutoML configuration object, specifying the task type as image classification. We also define the compute target, training data, and evaluation metric. We then submit the configuration to an experiment, which triggers the AutoML pipeline.

In Code Snippet 2, we deploy the trained model as a web service using Azure Container Instances (ACI). We define the deployment configuration and inference configuration, which specifies the entry script for making predictions. Finally, we call the deploy method to deploy the model, wait for the deployment to complete, and obtain the service endpoint.

With these code snippets, you can automate the end-to-end process of training and deploying computer vision models using AutoML in Azure. By leveraging the power of automation, you can save time and effort, allowing you to focus on solving real-world challenges.

In conclusion, automated machine learning (AutoML) makes it easier than ever to build computer vision solutions using Azure. By following the steps outlined in this article and leveraging the provided code snippets, you can streamline the process of training and deploying models. With Azure’s rich ecosystem of tools and services, you can unlock the full potential of computer vision for your applications.