The act of recognizing raw data (pictures, text files, videos, etc.) and adding one or more relevant and informative labels to provide context so that a machine learning model may learn from it is known as data labeling in machine learning.

Typically, data labeling begins with people observing an unlabeled piece of data. In a process known as “model training,” the machine learning model employs human-provided labels to discover the underlying patterns. 

As a consequence, you’ll have a trained model that you can use to create predictions based on new data.

While having humans in the loop to do tasks like labeling unstructured data is often an important part of generating training data for your model, the task’s arduous and time-consuming nature makes it unsuitable for small teams of highly trained and well-paid data scientists or engineers. 

This is why many companies outsource their data annotation initiatives to take advantage of lower-cost labor at scale.

Data annotation services are a proven approach for teams to increase productivity, save development time, and remain ahead of the competition by outsourcing them.

Choosing the ideal partner and collaborating with external teams, on the other hand, isn’t easy and comes with its own set of obstacles.

However, some characteristics distinguish the best data annotation firms from the rest of the pack. In this article, we will highlight some essential aspects to consider for choosing the right Data annotation vendor. 

Proficiency

When evaluating a company’s capabilities, make certain that they will be able to complete the project correctly the first time. Move on if you think they’ll require numerous iterations to do so. Look at their quality assurance methods to get a sense of how they handle data annotation.

Quick Scalability 

Your project may want more data annotation than you anticipated. This is why you must have faith in the service provider’s ability to expand up without sacrificing quality. 

Having complete control over the number of your workforce is also part of scalability. You could want to scale down just as much as you might want to scale up.

Ample Experience

Every business aspires to demonstrate that it has sufficient experience in its sector. Client logos, testimonials, and case studies give you a better understanding of the client’s background, solutions, and outcomes. 

News, press announcements, and recent blog pieces can provide insight into their overall industry understanding.

Technology and tools

Working with a data annotation company provides access to pre-built data annotation tools, which is one of the main advantages. This relieves your engineering staff of the burden of developing in-house tools from scratch. The company’s technology should help you save time and money by optimizing the data annotation process.

The finest annotation tools are easy to use, need little human intervention, and increase efficiency while retaining data quality. The platform should have a wide range of capabilities, support a wide range of project types, and include built-in project management and automation tools.

Speed

You might specify that a given amount of data be annotated within a specified amount of time. While everyone wants their data annotated as soon as possible, it’s crucial to sit down with each potential service provider and determine what can be completed in the time frame you need. 

If they give you an unusually short period to annotate all of the data, inquire about the quality control procedures in place.

Security measures

Due to the sensitive nature of their project, many firms are afraid to outsource their data annotation. Ask if a company’s security measures and processes have any international certifications or accreditations, such as ISO 2700, when examining their security measures and policies. 

This is an internationally recognized certification that is only given after an independent assessment of the company’s IT system to ensure that it meets international security standards. EU GDPR compliance, as well as whatever else they could have, are other items to search for.

Test their work

Typically, the service provider will let you annotate a tiny dataset to get a sense of their work quality and methods. You’ll be able to tell if the accuracy of annotation matches your needs after finishing a test data annotation project.

Know your requirements 

In some cases, data annotators will be required to have a specific background or degree. This is especially true in the case of medical data annotation projects and the healthcare industry, where employers frequently want personnel with a medical degree and prior job experience.

Cost

It’s useful to know how much you may anticipate spending for a product or service as a customer. Before going with any annotation firm, you should have a general concept of the price per task, especially if you have any financial constraints. However, we recommend that you choose an agency that does not provide a fee before reviewing your data, as prices can vary significantly based on the service or type of data.

The best data annotation firms are concerned with the return on investment for your team (ROI). Hire a vendor who can objectively assess your project, its cost, and potential solutions in terms of long-term return on investment.