In-House or Outsourced Data Labeling – Which Gives Better AI Results?

The development of computer vision-based relies heavily on quality annotated data to train the ML algorithms to identify various objects in the physical world. However, obtaining the needed annotated data, especially in the needed quantity, can be very difficult. This has led companies to try annotating the data themselves in-house, which is definitely not the most efficient approach. For example, startups usually need quick and affordable access to training data. As they grow, their demand for training data increases proportionally with the models they are developing. They usually cannot keep up with such internal demands, and therefore efficiency takes a big hit.

In this article, we will take a look at which companies with better results in in-house data labeling or hiring a data labeling company. 

What are the Benefits of Data Labeling Outsourcing? 

Outsourcing your data labeling and annotation services, for that matter, needs to be done to professional service providers. This way, companies can ensure flexible and responsive training data services. Experienced annotation companies can ensure flexibility for clients by leveraging the following core strengths: 

  • Freedom to focus on core responsibilities – Preparing the datasets is one of the challenging but critical aspects of training ML models. Having said this, you do not want your data scientists spending their time cleaning and labeling data since these are low-level and redundant tasks that could cause costly delays. By outsourcing data labeling, you streamline the entire process and ensure that the development occurs simultaneously. This way, you can make sure that your in-house team members focus solely on their core business tasks. 
  • High-level QA processes – One of the great things about working with experienced data labeling companies is that they place a lot of emphasis on making sure all of the tasks are done correctly. Usually, projects require a quality rate of 98%+, and it is important to have a QA process in place to make this level is reached. 
  • Annotate large quantities of data – A data labeling project could require you to label thousands of images and many hours of video. Needless to say, this is a very time-consuming process. It can be difficult and very costly to ask your in-house teams to waste their time on data labeling, which is why so many companies choose to use image annotation outsourcing to provide such work. 

Now that we see all of the benefits of outsourced data labeling let’s now take a look at when companies should opt for in-house data labeling. 

While there are many benefits to outsourcing your data labeling, there are some times when you should do the data labeling work in-house. Below you will find some situations when the in-house makes sense for you: 

  • An exclusive product is known only to company employees
  • The project has specific requirements available to internal sources
  • Time-consuming to train external service providers 

Also Read: Benefits Of Using The Windows 10 Key

When Should You Choose In-House Data Labeling? 

Trust Mindy Support With All of Your Data Labeling Needs

Mindy Support is a global company for data annotation and business process outsourcing, trusted by several Fortune 500 and GAFAM companies, as well as innovative startups. With nine years of experience under our belt and offices and representatives in Cyprus, Poland, Romania, The Netherlands, India, and Ukraine, Mindy Support’s team now stands strong with 2000+ professionals helping companies with their most advanced data annotation challenges.