How to Organize Data Labeling for Machine Learning: Practical Approaches

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Organizing data labeling for machine learning is not a one sitting job, yet a single error by a data labeler may cost you a fortune. Now, you probably wonder how do I get high-quality datasets without investing so much time and money? If you do proper responsibility division and estimate the time needed for a given task and the tools to help you get it out of the way in a short while, you’ll have nothing to worry about. In other words, prior organization of data labeling for a machine learning project is key to success.

Practices Worth Using While Annotating Images for ML

Annotating images for ML is a demanding business. Data labeling is an inevitable and most crucial stage in supervised learning. Data processed in this manner requires a human to map target attributes from historical data for an ML algorithm to find them. That said, data labelers must pay attention to detail because even the smallest error can potentially compromise the quality of the datasets and consequently affect the overall performance of the ML model.

Here are some of the best practices data labelers can use to annotate images for their predictive models:

  • In-house labeling
  • Crowdsourcing
  • Outsourcing to individuals
  • Outsourcing to companies
  • Data programming
  • Concluding thoughts

In-house labeling

In-house data labeling is considered the most accurate and efficient approach to annotating data. This internal approach gives you the chance to track the process in each stage, and assign tasks to your team appropriately. However, this approach may be slower compared to other practices discussed below but it is effective for data labeling companies with sufficient human labor, time, and finances.

  • Advantages: In-house labeling gives you the ability to control the whole process, and thus, create predictable good results. Following schedule is key when labelling data, and to be able to check on the team’s progress at any time, ensuring they are on schedule is gold.
  • Disadvantages: In-house labelling has a serious downside, it drags long. It is said good things take time, and it applies nowhere better than here. Your team will need time to label data meticulously to guarantee high-quality datasets. This is of course if your project is too big for your in-house team to finish faster.

Synthetic labeling

Synthetic labeling is where data is generated imitating the real data based on the standards set by a user. This labeling approach uses a generative model trained and validated on original data. Synthetic labeling can be applied in training ML models used for object recognition tasks. In complex tasks, for example, large training datasets are needed which require well-trained labelers. In addition, such a large amount of work usually has a short turnaround time, meaning generating a labeled dataset is the best option.

  • Advantages: Synthetic labeling saves time and costs because data can be generated faster, customized and modified quickly for specific tasks and to improve the model as well. In addition, data labelers can use non-sensitive data without necessarily having to ask for permission to use such data.
  • Disadvantages: This approach demands high-performance computing. The rendering process and further model training that goes into synthetic labeling requires high computational bandwidth. Secondly, the use of historical data might not guarantee a resemblance on the synthetic data. In this regard, ML models trained using this approach require further training using real data.


Instead of a data labeling company recruiting people, it can use a crowdsourcing platform with an on-demand workforce. On such platforms, clients register as requesters, create, and manage their ML projects with a single or more Human Intelligence Tasks (HITs). Some platforms providing such services are known to house a community of workers who can label thousands of images in a matter of hours.

  • Advantages:You want quick results? Crowdsourcing is your way to go. For labelers with huge projects and tight schedules, crowdsourcing comes handy. Equipped with powerful data labeling tools, this approach saves time and money.
  • Disadvantages: Crowdsourcing is not immune to delivering labeled data of inconsistent quality. A platform where income for members of the workforce depends on the number of tasks completed each day is prone to failing to follow task recommendations in a bid to complete as many tasks as possible.

Outsourcing to individuals

The internet has opened opportunities for freelancers to advertise their skills and experience and land on high-paying jobs such as data labeling. Freelancing companies allow clients to post jobs and recruit freelancers based on skills, hourly rates, work experience, and others.

  • Advantages: here you get the chance to interview the freelancers, and learn more about their expertise, and thus, you know who to hire and what to expect.
  • Disadvantages: Outsourcing to individuals might require you to create your own task interface or template, include comprehensive and crystal clear instructions for the freelancers to understand the tasks perfectly, and that is time-consuming.

Outsourcing to companies

There are readily-available outsourcing companies specialized in data labeling for ML. These companies are well-equipped with highly-trained staff who guarantee you high-quality training data.

  • Advantages: Outsourcing companies promise high-quality results ensuring their workforce can deliver it.
  • Disadvantages: This approach is costlier than crowdsourcing since most of them do not specify how much it would cost per project.

Data programming

Data programming eliminates completely human labeling. This technique has labeling functions that label data. A dataset produced through data programming approach can be used for training generative models.

  • Advantages: There is no need for manpower to label the data, a data analysis engine does the job automatically.
  • Disadvantages: This approach is known to give less accurate data labels, which then compromise the quality of the dataset and the overall performance of the ML model.

Concluding thoughts

Today’s innovators have embraced complex ML models with grit because they understand that high-quality data is all that matters. While data annotation tools are readily-available on the internet, finding the right annotation tool is another daunting task. Data science teams need to know which software best suits a particular project in terms of the overall cost and functionality. In addition, data labelers have found new ways of semi-automating the labeling process, partly removing or adding to the manual labeling techniques. That said, the future will rely largely on the development of more efficient automated data labeling processes that reduce human involvement but at the same time proving high-quality training datasets for ML models.

About the Author

Melanie Johnson, AI and computer vision enthusiast with a wealth of experience in technical writing. Passionate about innovation and AI-powered solutions loves sharing expert insights and educating individuals on tech.

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