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Why outsource your data labeling projects to an external workforce?

Data labeling is an essential step in machine learning. It will allow to identify raw data (images, text files, videos, etc.) and to add one or more meaningful and informative labels to provide a context so that a machine learning model can learn. This is an essential step in machine learning which represents, according to a Cognilytica study, 80% of AI projects.
Find out in this article why you should outsource the annotation of your data to an external task force.

What is a labeling workforce?

To annotate data, companies can decide to do it in-house or outsource it to a specialized task force.

The workforce you have chosen to classify your dataset is known as the labeling workforce. When you employ an external workforce, you also establish work teams, which are collections of your workforce members who are tasked with carrying out particular tasks. Each task can have one or more work teams assigned to it depending on the project requirements.

Main use cases handled by a specialized labeling workforce

Image annotation :

Lane and Parking Area Detection:

A labeling workforce can supply training datasets to teach your computer vision systems to identify lanes and lane markers for determining the drivable area to determine the parking area.

Object Recognition:

You can create training datasets that assist your autonomous vehicle models in correctly identifying surrounding things.

Semaphore Analysis:

Labelers can use bounding boxes to annotate traffic signals and signs to make it easier for autonomous vehicle models to recognize these signals.

Product recognition:

For retail establishments, data Labelers can provide pixel-level precise product annotation services.

but also :

Text annotation, data processing.. find out more uses cases here

Pros and cons of using an external labeling workforce vs in house

Companies may decide to annotate data in-house or outsource it to a workforce.

Learn about the benefits that exist for each solution :

In house labeling workforce Privacy:
When your labeling is done in-house, you get full control over the entire process and therefore do not run the risk of having confidential business information exposed to a third party. You can be sure that your contractor will not later sell the labeled data to a competitor.
Quality control:
Strong quality control is an advantage of doing your labeling in-house as you would be able to supervise the execution of the entire project based on your preferred quality parameters.
Long-term dependability and success can be produced through a self-developed, consistent annotation procedure. It also creates a feedback mechanism that continuously promotes best practices in the collection of data for use in AI or ML models, which is something that all businesses eventually need.
External labeling workforce Manage high volume data labeling:
For a typical AI project, thousands of data sets must be correctly classified and annotated, which is a labor-intensive process. However, the amount of data required is very project-specific, thus an increase in demand may cause the deadlines for your in-house teams' milestones to be extended. You might need to enlist the aid of additional teams as the volume of data rises, to improve overall productivity.
Eliminates internal bias:
A hand-selected annotation workforce guarantees trustworthy quality control free from any internal bias that may have developed by the internal team within a company.
Economical: Outsourcing the labeling of data is more cost-effective than doing it in-house since external labelers can annotate a lot of data quickly and precisely in a variety of formats. Training:
With a group of knowledgeable data labelers working together on your task, you can be sure that it will be finished quickly and to your standards. These experts can improve your data labeling for ML and AI applications by building on their past knowledge of different data sets and honing their data labeling abilities.
24/7 availability:
You will have access to a highly accessible, quickly deployable, and 24/7 global annotation workforce..

How to choose the right labeling workforce for your project? 

  • Size: You need to consider the scope of your project how large or small it is as this will affect the number of people you'll need on your data labeling team. Basically, the larger the scope of the work, the larger the team.
  • Diversity: Teams with more diversity are more effective and perform better. A diversified workforce offers a breadth of expertise, experience, and working styles that can improve problem-solving skills and increase output.
  • Language: You want to make sure that the team you and the team you're hiring understand the same languages to avoid any error or ambiguity in communication that could affect the quality of the project.
  • Location: Location is an important selection criterion for several reasons, the first is that it induces a certain time zone depending on the country and can therefore facilitate communication with project teams. Moreover, the culture of the workforce country is also very important, the annotator will better understand the context of the annotation if she.he is familiar with the environment (example with the annotation of road signs which are different depending on the country, or in the annotation of meal trays where an annotator can confuse a starter with a dessert if she.he does not know a little about the culinary culture of the country.
  • Academic background: A workforce can be made up of different profiles and have different and complementary academic skills. There is a huge number of students among the workforce and the latter, depending on their studies, can annotate data in an even more qualitative way if they are familiar with the context. For example, a dermatology student will be able to annotate moles in a more qualitative way because she will have a better knowledge of the subject.
  • Training : the training of annotators is essential for successful labeling: on the tools used, the expected precision and on all the specificities relating to each project.

How to choose the best partner to provide you with a labeling workforce?

While in-house labeling is frequently regarded as the pinnacle of data labeling, depending on the phase of your business, it may not always be feasible. If you need to annotate a high volume of data, outsource the labelling but to a reputable data labeling service company or to crowdsource which will have the expertise, all the tools and a trained and qualified workforce.

While there are benefits to crowdsourcing, you frequently wind up spending more time overseeing quality control than you would have otherwise. The benefit of using a data labeling service is that a qualified team handles everything, allowing you to essentially concentrate on other internal initiatives. The best part is that quality is guaranteed and possibly costs less than you expect.

So, if your data requires labeling and it's delaying your AI/ML project, think about consulting with a company like Isahit that offers data labeling services.

How much does it cost to recruit a labeling workforce ?

Businesses can save money by outsourcing data annotation services rather than recruiting and developing a diverse internal team. The cost of the internal team is high due to the infrastructure, technology, and staff salaries that must be paid.

Compared to the pilot internal team, a third-party service provider provides fast access to an expert team. A skilled team can produce useful datasets for upcoming queries or requirements by understanding the algorithm model of their client. An outsourcing company provides constant accessibility to datasets to train ML algorithms, which aid computers in deciphering the presented digital content's entities.

When is it time to use a labeling workforce?

It is time to use a labeling workforce when:

1. In-house costs are unsustainable and cannot scale: Because of the high worker pay in advanced economies, labeling data internally is particularly expensive. These expenditures can increase to the extent where it is no longer practical to keep labeling in-house for increasingly large datasets. This issue is a common pain point when it comes to internal data labeling.

2. Difficulty recruiting and training labelers: It's not always possible to hire new labelers if your internal labeling team has decreased in size or is insufficient. This is because new personnel need training in order to manufacture labels of a high enough caliber.

Why choose isahit to use an external labeling workforce 

The Only Ethical Choice : Isahit is the only data labeling company that places impact at the heart of its model. Convinced that Tech can be a lever for social inclusion, they offer women around the world the opportunity to work remotely and gain skills through free and multidisciplinary training. BCorp certified since 2021, isahit is revolutionizing the world of labeling and data outsourcing by making it ethical.

A wide, diverse and qualify workforce : More than 1500 women are working every day - and as many different profiles - all having the same goal: make your annotation projects a success. They come from 44 countries, speaks 16 languages, cover 5 timezones and have different skills and academic background. In addition, isahit provides them with a very complete and unique training program for each client project. Our culture also supports a broad representation in machine learning, which strengthens the objectivity and ethics of your AI model.

Workforce management platform: Isahit got the most powerful workforce management platform to assist you in developing the best workflow for your projects. This allows us to cut labor expenditures dramatically without sacrificing performance. In reality, the efficient administration of our staff encourages people to produce their best work.

Security : You can rely on Isahit to provide a secure environment free of data security breaches. Since systems with inadequate encryption algorithms are vulnerable to hackers, we are a corporation that prioritizes data protection.

Experience: Many companies undervalue the knowledge or abilities required to provide data labeling services because they believe this is a straightforward task. To avoid human error, which is common but can accumulate and have serious long-term effects, this talent necessitates accuracy and close attention to detail. Due to their lack of adequate resources and equipment to properly label your data, inexperienced providers may potentially incur expensive delays. As of today, isahit have supported more than 350 customers, from various industries and managed more than 4000 Data Labeling projects, from #skinrecognition or #foodrecognition to #predictivemaintenance.In addition, our annotator are highly qualified and experienced in data labeling.

Quality: Quality assurance is a vital aspect of contracting your data labeling. You can be confident that your data is in excellent hands because all of isahit annotators are competent, well-trained, and correctly integrated in the area that your data services. Our staff can adapt rapidly to your requests for workflow adjustments, be open and effective in communicating with you through a tight feedback loop.


You have seen in this article, when it is useful to ask yourself the question of externalization for the labeling of your data. Several solutions exist, but only one prevails if you want to generate a positive impact with annotators and benefit from the best possible quality in your annotations!

In order to assist you in creating machine learning models that perform better, Isahit provides a variety of data labeling services. Some of the biggest brands in the world and the fastest-growing businesses have trusted them as a partner.

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