Data Labeling in Machine Learning with Python: Explore modern ways to prepare labeled data for training and fine-tuning ML and generative AI models

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Publisher ‏ : ‎ Packt Publishing (January 31, 2024)
Language ‏ : ‎ English
Paperback ‏ : ‎ 398 pages
ISBN-10 ‏ : ‎ 1804610542
ISBN-13 ‏ : ‎ 978-1804610541
Item Weight ‏ : ‎ 1.52 pounds
Dimensions ‏ : ‎ 9.25 x 7.52 x 0.82 inches

Data labeling is a crucial step in the machine learning process, as labeled data is necessary for training and fine-tuning models. In this post, we will explore modern ways to prepare labeled data for machine learning and generative AI models using Python.

One popular tool for data labeling in Python is the LabelImg library, which allows users to easily annotate images with bounding boxes, polygons, and points. This can be particularly useful for object detection tasks in computer vision.

Another useful tool is the Label Studio library, which provides a flexible and customizable platform for labeling various types of data, including text, images, and audio. Label Studio supports active learning, which can help optimize the labeling process by focusing on the most informative data points.

For more complex labeling tasks, tools like Snorkel and Prodigy can be used to create labeled datasets using weak supervision or active learning techniques. These tools can help streamline the labeling process and improve the quality of labeled data for training machine learning models.

In addition to these tools, there are also pre-trained models and APIs available for tasks like sentiment analysis, entity recognition, and image classification, which can be leveraged to quickly label data for machine learning tasks.

Overall, data labeling is a critical step in the machine learning process, and using modern tools and techniques can help streamline the process and improve the quality of labeled data for training and fine-tuning ML and generative AI models.
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