Modern organizations rely heavily on data, which also serves as the cornerstone for AI-powered
solutions. But raw data is exactly that—raw—when it comes to that. For it to be useful at all, it must
be properly labeled and organized.

“Data is the new oil”- Clive Humby

Data labeling fills that need. Among the many advantages of labeled data are increased machine
learning algorithm accuracy, improved user experiences, and improved decision-making.

Machine learning engineers and data scientists are not magicians. A lot of labor is required to get
computer vision projects production-ready, and data operations teams—a group of dedicated
professionals—work tirelessly behind the scenes to make this happen.

Teams dedicated to data operations, also known as data labeling operations teams, are essential to
the successful execution of computer vision and artificial intelligence initiatives particularly in data-centric projects. An automated, AI-backed labeling and annotating tool is useful and vital, but a project needs a team and a strategy to make sure the work gets done.

In a different context, a data labeling operations function is crucial to guaranteeing that data labels
and annotations are of the highest caliber.

Why is Data Labelling Necessary for Computer Vision Projects?

Data labeling, sometimes referred to as data annotation, is a collection of operations used in
computer vision and other algorithmic models to take unlabeled, raw data and apply labels and
annotations to image or video-based datasets (or other data sources).

Accuracy and quality are essential for computer vision applications. Inaccurate results will be
produced if you input movies or photographs that are of poor quality, incorrectly tagged, and
annotated.

There are various approaches to implementing data labeling. Your annotation team may be able to
handle manual annotation if you only have a small dataset. Going frame by frame through every
picture or video. To speed up the procedure and enhance quality and accuracy, automated
workflows and an automated data annotation tool are helpful in most circumstances.

Why is it that data labeling operations are so important?

An algorithmic model, such as a computer vision model or anything contained in an image or video, is displayed using annotations and labels. Algorithms are blind. We must demonstrate to them. Algorithms are trained by people through labels and annotations to recognize, comprehend, and place objects in photos and movies.

All of this is made possible by data labeling operations. Making data training and subsequently, production-ready requires a lot of labor, including quality control, data pipeline setup and maintenance, data cleaning chores, and testing models for bias and mistakes.

How to select an efficient Data Labelling Partner?

Many teams and project managers debate whether to design or purchase data labeling solutions.
Creating your own data labeling and annotation software could seem like a benefit. The fact that it
requires a significant amount of time and money is a drawback.

After developing software internally, you’ll need engineers to keep it updated and maintained. What
happens if you require more features? You have less freedom to scale and adjust. Although, many
tools are available under an open-source license. Most, however, don’t fit the proper requirements
for commercial data operations teams. Associating with an effective data labeling partner, such as
Data Labeler is far more apt and time-efficient than developing your solution.

About Us:

Developing a workflow for data labeling activities that work well first requires starting small, learning
from minor setbacks, iterating, and then scaling.

Now utilize Data Labeler to Create More Efficient and Streamlined Data Label Operations You can create data labeling operations more efficiently, safely, and at scale with Data Labeler, an automated tool used by top AI teams globally.

Wondering how to contact us? Click here!