Data Annotation

Discover useful insights into the challenges of data preparation to ensure that your next artificial intelligence project is a success.

As the list of managed data service providers grows, how do you decide which to trust? Discover key steps for selecting the right data annotation company for your project.

Data annotation is the invisible infrastructure behind our AI-powered future. Learn more about this industry and why it’s never mattered more.

In this article we cover five common approaches to data labeling for machine learning, along with pros and cons for each.

Wondering which image annotation types best suit your project? In this article, we introduce five types of image annotation and their applications.

Learn about product categorization (also known as product classification) and how it’s crucial to the eCommerce industry.

Text analysis tools offer a multitude of benefits, but how do they work? We look at types of AI text analysis, their use cases and how to get started.

Capturing enough accurate, quality data at scale is a common challenge. Discover four ways to source raw data for machine learning, and how to go about annotation process.

Are you evaluating your annotation team for quality? Read why self-agreement checks are critical for a proper evaluation.

To train NLP algorithms, large annotated text datasets are required. Learn more with a brief introduction to five common types of text annotation.

Looking for an introduction to audio classification? In this article we discuss different types of audio classification and their use in machine learning projects.