With an increasing demand for sophisticated models capable of understanding diverse data types, the importance of accurate annotations cannot be overstated. Azure Machine Learning offers a powerful solution, providing a data labeling interface designed to streamline the annotation process for images, text, and audio. Azure Machine Learning’s data labeling capability facilitates the process of creating, managing, and monitoring data labeling projects and enables seamless collaboration among data scientists, domain experts, and annotators.
Let’s look at the benefits of data labeling with Azure Machine Learning.
Benefits of data labeling with Azure Machine Learning
Data labeling is used to train machine learning models and helps to improve the accuracy of these models. Azure Machine Learning data labeling tools can be used to create image, text, and audio labeling projects.
Azure Machine Learning data labeling tools provide the ability to manage and monitor labeling projects seamlessly from within the studio web experience and reduce the back-and-forth process of labeling data offline.
After labeling the data in an Azure Machine Learning data labeling project, the labeled data can be exported to Azure Blob storage using the Export option in the project. From there, this labeled data can be integrated as a dataset in the Azure Machine Learning pipeline for training machine learning models.
Data that is labeled on-premises using other open source tools, such as Label Studio and pyOpenAnnotate, also can be integrated with Azure Machine Learning by creating a dataset from local files.
Let us see how to create a labeling project, how to upload data, how to create a labeling task, and how to manage and monitor the labeling project using Azure Machine Learning data labeling tools.
Data labeling steps using Azure Machine Learning
Here is an overview of the steps to create image, text, and audio labeling projects using Azure Machine Learning data labeling tools:
- Create a labeling project: Sign into Azure Machine Learning and create a new labeling project. You can choose to create an image, text, or audio labeling project.
- Create a labeling task for your data: You can choose to create a classification, object detection, instance segmentation, or semantic segmentation task.
- Upload data: Upload the data you want to label to your labeling project. You can upload data from your local machine or from a cloud storage account.
- Label your data: Use the labeling tool to label your data. In the case of machine learning-assisted data labeling, machine learning algorithms may be triggered to assist with the data labeling task. After some data has been labeled manually, machine learning algorithms automatically group similar images on the screen with the suggested label name.
- Manage and monitor your labeling project: Monitor the progress of your labeling project and tasks from within the studio web experience. You can also export your labeled data as an Azure Machine Learning dataset.
In the following sections, we are going to discuss data labeling for image, text, and audio data using Azure Machine Learning.