Data Annotation

Data preparation is the most time-consuming part of machine learning. Here are a few tips for getting it right.

Agriculture data is complex. Annotating agtech data often requires help from agronomists. We help Hummingbird Tech overcome that AI product development hurdle.

Some data annotation tools won't be a good fit for your AI and machine learning project. Keep these six important features in mind as you evaluate tool providers.

The data annotation. One emerging feature is automation, also known as pre-annotation or auto labeling. This article will focus on some of its benefits and drawbacks.

Your choices about tooling and workforce will be important factors in your success as you design, test, validate, and deploy any ML model.

Natural language processing (NLP) is among the fastest growing AI technologies and one of the most difficult to develop. In this article, we'll share lessons learned over 10 years ...

Natural language processing (NLP) is among the fastest growing AI technologies today. It’s also one of the most difficult to develop. This is the first in a two-article series ...

Data can be gold for businesses, but there’s a catch: it must be uncovered, clean, and structured. Our latest ebook explores common data-quality issues and clean-data practices ...

In our new white paper, we explore AI trends, the importance of choosing the right tools, and how to strategically deploy people in your tech-and-human stack.

In the French presidential election, Macron’s campaign had the foresight to see how combining data, analytics, and human intelligence could give him a competitive advantage.

AI is building technology that behaves like a human, whereas Machine learning is a subset of artificial intelligence that uses algorithms to learn from data sets.