Data Labeling in Azure Machine Learning: A Comprehensive Guide for Image and Text Data

Data labeling is a crucial step in any machine learning project, as it provides the ground truth for training and evaluating models. However, data labeling can also be a tedious, time-consuming, and error-prone task, especially for large and complex datasets. To address this challenge, Azure Machine Learning offers a data labeling tool that enables you to create, manage, and monitor data labeling projects with ease and efficiency.

In this presentation, you will learn how to:
- Use the data labeling tool in Azure Machine Learning to label image and text data for various machine learning tasks, such as classification, object detection, instance segmentation, semantic segmentation, and named entity recognition.
- Leverage machine learning-assisted data labeling and human-in-the-loop labeling to accelerate and improve the quality of your labeling process.
Coordinate data, labels, and team members to efficiently manage labeling tasks and track progress.
- Review and export the labeled data as an Azure Machine Learning dataset for further analysis and modeling.
- Integrate the data labeling tool with other Azure Machine Learning services, such as MLflow, AutoML, and pipelines, to streamline and automate your machine learning lifecycle.

Join this session to discover how data labeling in Azure Machine Learning can help you prepare high-quality data for your machine learning projects.

Use the data labeling tool in Azure Machine Learning for various tasks like classification, object detection, and named entity recognition. Accelerate and improve labeling quality with machine learning-assisted and human-in-the-loop labeling. Coordinate data, labels, and team members to manage tasks and track progress. Review and export labeled data for further analysis and modeling. Integrate the tool with other Azure Machine Learning services to streamline your machine learning lifecycle.