The real-life application of facial recognition for security, autonomous vehicles, and even robot assistants is no longer restricted to the sci-fi movie realm. These life-altering technologies are already here and they are bound to shape our future in a major way. Computer vision AI applications are ever leading us in this direction. 

To actualize successful AI and ML applications, models rely on accurately labeled/tagged data. For instance, in order to build a computer vision application, massive loads of visual data must be annotated and fed into the model. This is what is referred to as image annotation. This human-powered task of labeling images can be tedious, overly expensive, and time-consuming.

Employing an in-house data annotation team can be a monotonous task that comes with its own set of challenges. As a consequence, we find that many businesses prefer to outsource some if not all of their data training needs. These include; image annotation, data collection, data validation, live project monitoring, etc.

Advantages of Outsourcing Data Annotation


With a reliable image annotation outsourcing team, you rid yourself of the constraints that come with data volume upheavals. One can easily ask the outsourcing firm to scale up or down depending on your current needs.


Data labeling companies come with a breadth of experience that places them in a unique position. They can better advise on the right talent, tools, and approach that fits your project. 

Saves Time

Data labeling and collection consume a huge amount of time and it takes even longer to train a team to do the job. By partnering with an experienced outsourcing company, the task of recruiting and training the team is passed on to them. This frees your time which can be better utilized in other aspects of running your company.

There are some very important points to go through before settling on a data annotation outsourcing partner. With the ever-increasing number of image annotation outsourcing companies, choosing the right fit can be a daunting task. 

Follow these steps to find your way through the murk.

Step 1: Realize your needs

For every computer vision application or model, there is a specific annotation technique to actualize it. You must first determine what your AI model use case is and the problems it intends to solve.

Below are some questions to ask yourself when selecting the right vendor.

  • What sort of data are you operating?
  • What sort of image annotation fits your project? (text annotation, image annotation, video annotation, etc)
  • What is your budget?
  • How do you determine project efficiency?

Being knowledgeable about your needs places you on a solid footing to effectively pass on your requirements to potential partners.

Step 2: Go for the right vendor

Selecting the right partner can make or break your AI/ML project. Below are some questions to help you select the right outsourcing partner. 


  • Industry Knowledge and Experience – Given the different types of annotation (image, video, text, etc), annotating data can vary depending on the type of annotation needed. Let’s say your AI model requires video annotation, be careful to select an outsourcing company with relative experience on the same before committing.


  • What platforms/tools do they employ – There are many annotation tools and platforms out there in the market. It is important to interrogate every potential outsourcing partner’s knowledge on this as they can advise on the best tool that meets your needs. 


  • Are they committed to ethical AI and Social Impact – Since you are basically offshoring your work, you want to ensure that you are making the most positive impact on the people that handle your project. Enquire on how annotators are remunerated and their overall benefits. From experience, most outsourcing companies are happy to share this information with a potential partner. 


Step 3: Monitor and Manage Expectations

To ensure the success of outsourcing data annotation, proper quality assurance is paramount. The outsourcing company must have layers upon layers of quality checks to guarantee high-quality datasets.

Measure the vendor’s ability to produce high-quality datasets by posing questions like:


  • Project Trial – Most outsourcing companies offer a free trial for clients to measure their quality and overall professionalism. Before committing to anything long-term, first, send the potential vendor a sample of the expected work and judge their output. If the quality satisfies your needs, then you can proceed to partner.


  • The number of Annotators/Capacity – This is important to ask for when you want to scale your team. You don’t want to commit to a vendor who can only commit a small number of annotators. Equally important, always go for the vendor who can easily scale down the team when the circumstance calls for it.


  • Pricing – It’s important to find out the most suitable pricing model for a successful partnership. This can be on a per-hour basis or per task/image. Depending on which one suits you best, always make it clear to the potential vendor.

Impact Outsourcing prides itself on providing humans in the loop, crucial for actualizing Artificial Intelligence and Machine Learning. We seek to create long-term meaningful employment for thousands of marginalized youth and women through data annotation jobs. With our years of experience in data collection, data curation, data labeling, and live project monitoring, we have birthed a quality-first attitude to project management. Try us today and we’d be happy to be your number-one outsourcing partner.

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