How To Organize Data Labeling For Machine Learning: 5 Rules To Consider

How to Organize Data Labeling for Machine Learning: 5 Rules to Consider

by Melanie Johnson — 3 years ago in Machine Learning 4 min. read

Without a doubt, data labeling is one of the most challenging processes of data management. Most data is in unlabeled form, and this is a huge task for most artificial intelligence project management teams. Data labeling is a process that demands a lot of time, money, and effort. This process can take a lot of energy that can be used for more strategic plans if it’s managed correctly.

Even though data labeling is not rocket science, it is something to be taken seriously. Labeled data is a necessity for machine learning or supervised learning. AI algorithms can only be trained on data with predefined target attributes.

Therefore, it is essential to pay attention to the data labeling process since any error or mistake can affect the accuracy of the prediction the machine learning model makes when trained using that specific dataset. Trust me, you don’t want to go through the stress of training and deploying models with unlabeled and organized data.

Do you have a lot of data to label for machine learning? Do you want to label your dataset effectively and efficiently? Then you should keep on reading.

This guide will teach you how to get started with data labeling and organize it. You will also learn the major rules to be considered during the whole process.

What is Data Annotation?

Data annotation or data labeling, used interchangeably, highlights the features and classes on raw data to ease pattern analysis.
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Why do you need to Organize Data Labeling for Machine Learning?

Machine learning models won’t train themselves, you’ll need to label your data which you can later use to train your models. Training your model will make it easy for the model to recognize and understand the patterns in the data for appropriate output delivery. Labeling your datasets will make machine learning models identify recurring patterns in the new input of unorganized data.

Data labeling for machine learning is a time-consuming process. You’ll need to identify and iterate data features before training your models. If you are working on a computer vision project, for instance, you will need to take the images or video frames and go over them one by one outlining the objects on each image and giving them classes for the model to have information regarding that data to train on.

Eventually, this will help you improve model performance. But you need to note that your labeling requirements increase as the volume of data to be labeled increases.

How to Organize Data Labeling

Data labeling can be done automatically or manually. You can label data manually by using crowdfunding, contractors, and your employees. To label data automatically, you will need the right tools. Like any other process, labeling data will automatically save you so much time, energy, and money.

To organize data labeling for machine learning, you’ll need to select the right software, personnel, and approaches that will be used for your specific project. Majorly, you can choose any of the following data labeling approaches for labeling your datasets:

In-house labeling

The workforce of your organization is one of the good options for data labeling. However, they don’t necessarily need to be data labeling experts or employed to do data labeling specifically. If you have the time and resources, you can consider training and managing a team of data labelers.

You can easily conduct a sentiment evaluation of your brand’s social image with in-house labeling. Through this approach, you can assess the prestige and progress of your organization without outsourcing the task.
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In situations where you have a large volume of data to label or your employees can’t manage the data labeling, you can use the services of a third-party agency to handle the data labeling for you. There are so many crowdsourcing platforms that can manage your data labeling processes quickly and efficiently.


These are temporary employees that are usually experts in data labeling. Instead of putting so much pressure on your employees, you can hire freelance data labelers that can analyze and organize your datasets to give the desired results.


If you’re doubting the capability of your workforce or temporary employees in managing the labeling of your datasets, you can outsource the project to another company that specializes in data labeling. Ensure that you do your research about the company. Choose the company that has the capacity to manage your data labeling the way you want it.

Rules to Consider During Data Labeling

Training is vital for developing a useful machine learning model, but guess what? You can’t train a machine model without high-quality training data. To label your data without tampering with the quality, you should consider the following rules:

Manage dataset quality consistently

It’s widely accepted that the quality of labeled data depends on the quality of the dataset. To avoid the quality issues associated with labeled data, ensure that your labelers are skilled to provide quality annotations.

Choose the right tools

You need highly effective tools to complete a data labeling process without tampering with the quality of the data. It is imperative to choose data labeling tools that will be appropriate for the volume of unstructured data to be labeled.

When you’re choosing the tools, make sure that you select the right tools that can meet your company’s needs. Choosing a user-friendly tool that will simplify the process is highly recommended.
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Adhere to data privacy requirements

Presently, many data compliance rules must be considered when labeling raw data. These data regulations are majorly applicable to personal data like the faces of people. Ensure that you research the data regulations that apply to the unstructured datasets in your possession.

However, some data requirements are related to the processing of the data. It is expected that unidentified data will be processed by companies lawfully. Make sure that your data is stored on reliable devices and managed on approved premises.

Pay attention

Imagine making tons of errors while labeling and organizing data for days just because the labelers are distracted. Making errors or mistakes while labeling data can be disastrous, it can affect the quality of the labeled data and the general performance of the model. Hence, labelers need to pay attention to the data labeling process.

Minimize costs

You can reduce the overall costs of data labeling by using cost-effective services. However, make sure you’re not sacrificing quality for cheap services and tools. The best thing is to find an affordable tool or service provider that will offer great value for money.

Overall, the significance of high-quality labeled data in machine learning cannot be overemphasized. Attention should be given to the main factors that can affect the quality of the labeled data.

You can label your datasets properly if you choose the right team that can manage the process adeptly, use the right tools, and estimates the amount of resources needed to complete the process correctly.

Melanie Johnson

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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