Data Labeling

Supervised learning requires a lot of labeled data. Here’s what it takes to design a high-performance data labeling pipeline for machine learning.

Even in uncertain times, you’re swimming in an ocean of data. If you’re using AI, how you process and use that data will determine the future of your business.

Your in-house data scientists shouldn't be doing tedious data labeling work for machine learning projects. They should be focusing on more important innovation.

Not all outsourced data labeling partners are a good fit for every AI project. Here are 5 things you need to consider before, during, and after vendor evaluations.

It takes a lot of time and resources to prepare and label data. Learn why outsourcing the data preparation to a managed workforce partner is a good business decision.

The level of data quality you'll receive from data labeling providers depends on several workforce, QA and tooling factors. Here are 6 ways some data labeling providers put your ...

The people, processes, and tools used by outsourced data labeling partners make a big difference in final data quality. Here are 3 signs that you'll receive quality work from your ...

Data quality is clearly paramount for a successful AI project, but how difficult is it to achieve?

Achieving a high level of accuracy in data labeling is vital. This concept can be understood if we think about a mural of Rubik’s Cubes®.

Any problem (like a Rubik’s cube®) is solvable with a documented process.

How solving a Rubik’s cube® is like labeling your unstructured data.

CloudFactory partner Scientia shares the AI opportunity and the importance of quality data for machine learning.

Gartner predicts 85% of AI projects will fail. One of the leading reasons is low-quality data labeling. High-performing machine learning algorithms require high-quality data. ...

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

Crowdsourcing seems to offer a cheap option for training machine learning models, but it’s rarely as inexpensive as it seems. Here are some of the hidden costs of the crowd.

AI makes information easier to find for attorneys and their opponents. That levels the playing field. It fundamentally changes the way work is done in the legal profession, where ...