The ultimate guide to data labeling: How to label data for machine learning

Artificial Intelligence (AI) is driving the future, and you should be ready for it to have a competitive advantage.

Machine learning (ML) is a subset of AI that provides software applications with the ability to detect patterns and make accurate predictions. ML gave us self-driving cars, email spam filtering, traffic detection, and more.

To train the highest-quality ML models, you need to feed their algorithm with accurate labeled data.

This blog post covers everything you need to know about data labeling to make informed decisions for your business. Here are the questions that this blog post will be answering:

  • What is data labeling?
  • How does data labeling work?
  • What are some data labeling best practices?
  • How do companies label their data?
  • Do I need a tooling platform for data labeling?
data annotation

What is data labeling?

Data labeling is the task of identifying objects in raw data, such as videos and images and tagging them with labels that help your machine learning model make accurate predictions and estimations. For example, data annotation can help autonomous vehicles stop at pedestrian crossings, digital assistants recognize voices, and security cameras detect suspicious behavior.

How does data labeling work?  

Data collection

Start by collecting a significant amount of data: images, videos, audio files, texts, etc. A large and diverse amount of data guarantees more accurate results compared to a small amount of data.

Data tagging

Data tagging consists of human labelers identifying elements in unlabeled data using a data labeling platform. They can be asked to determine whether an image contains a person or not, or to track a ball in a video.

Quality assurance

Your labeled data must be informative and accurate to create top-performing ML models. Make sure you have a quality assurance (QA) process in place to check the accuracy of your labeled data or else your ML model will fail to operate successfully.

collaboration and quality management system

Model training

To train an ML model, feed the ML algorithm with labeled data that contain the correct answer. With your newly trained model, you can make accurate predictions on a new set of data.

What are some of the best practices for data labeling?

Apply these tried and tested data labeling practices to run a successful project.

Collect diverse data

You want your data to be as diverse as possible to minimize bias. Suppose you want to train a model for a self-driving car. If the data you choose to train your model with was collected in a city, then the car will have trouble navigating in the mountains. For this reason, make sure you get images and videos from different angles and lighting conditions.

Collect specific data

Your data needs to be specific to not confuse the model. This sounds contradictory to the previous point, but it is important to feed the model with the information it needs to operate successfully. So if you’re training a model for a robot waiter, use data that was collected in restaurants. Feeding the model with data collected in a mall, airport, or hospital will cause unnecessary confusion.

Establish a QA process

Integrate a QA method into your project pipeline to assess the quality of the labels and guarantee successful project results. There are a few ways you can do that:

  • Audit tasks: Include “audit” tasks among regular tasks to test the labeler’s work quality. “Audit” tasks should not differ from other work items to avoid bias.
  • Targeted QA: Prioritize work items that contain disagreements between annotators for review.
  • Random QA: Regularly check a random sample of work items for each annotator to test the quality of their work.

Apply these methods and use the findings to improve your guidelines or train your annotators.

Set up an annotation guideline

Write an informative, clear, and concise annotation guideline that defines annotation and tool instructions to avoid possible mistakes from the beginning. Consider illustrating the labels with examples: visuals help annotators and QAs understand the annotation requirements better than written explanations. The guideline should also include the end goal to show the workforce the big picture and motivate them.

Find the most suitable annotation pipeline

Implement an annotation pipeline that fits your project needs to maximize efficiency and minimize delivery time. For example, you can set the most popular label at the top of the list so that annotators don’t waste time trying to find it. You can also set up an annotation workflow to define the annotation steps.

Keep communication open

Establish a line of communication with the workforce, and keep in touch with key stakeholders. You can build effective communication by setting up regular meetings and creating a group channel.

Provide regular feedback

Communicate annotation errors with your workforce for a more streamlined QA process. Regular feedback helps them get a better understanding of the guidelines and achieve higher quality results. Make sure that the feedback is consistent with the provided annotation guidelines. If you encounter an error that was not clarified in the guideline, consider updating it and communicating the change with the workforce.

Run a pilot project

Always test the waters before jumping in. Put your workforce, annotation guidelines, and project processes to test by running a pilot project. This will help you determine time to completion, evaluate the performance of your labelers and QAs, and improve your guidelines and processes before starting your project.

How do companies label their data?

Data labeling costs time and money. Consider your budget and your desired project delivery time before choosing how you want to get your data labeled.  

  • In-house: Manage your data annotation internally by using existing resources and employees. While in-house data labeling costs less, gives you more control of your projects, and ensures the safety of your data, it can also be time-consuming.
  • Outsourcing: Let expert data labeling services handle your projects. Outsourcing saves you time while guaranteeing quality results.
  • Crowdsourcing: If you’re lacking internal resources, consider crowdsourcing your data annotation projects to a trusted third-party platform.

If you choose to outsource or crowdsource, consider implementing a robust management process to maintain control of your project.

What should I look for when choosing a data labeling platform?

High-quality data requires an expert data labeling team paired with robust tooling. You can either buy the platform or build it yourself if you can’t find one that suits your use case. What should you look for when choosing a platform for your data labeling project?

data labeling

Inclusive tools

Before looking for a labeling platform, think about the tools that fit your use case. Maybe you need the polygon tool to label cars, or perhaps a rotating bounding box to label containers. Make sure the platform you choose contains the tools you need to create the highest quality labels. Think about a couple of steps ahead and consider the labeling tools you might need in the future, too. Why invest time and resources in a labeling platform that you won’t be able to use for future projects? Training employees on a new platform costs time and money, so being a couple of steps ahead will save you a headache.

Integrated management system

Effective management is the building block of a successful data labeling project. For this reason, the selected data labeling platform should contain an integrated management system to manage projects, data, and users. A robust labeling platform should also enable project managers to track project progress and user productivity, communicate with annotators regarding mislabeled data, implement an annotation workflow, review and edit labels, and monitor quality assurance.

Quality assurance process

The accuracy of your data determines the quality of your training model. Make sure that the labeling platform that you choose features a quality assurance process that lets the project manager control the quality of the labeled data. Note that in addition to a sturdy quality assurance system, the data annotation services that you choose should be trained, vetted, and professionally managed.

Guaranteed privacy and security

The privacy of your data should be your top priority. Choose a secure labeling platform that you can trust with your sensitive data.

Technical support and documentation

Make sure the data annotation platform you choose provides technical support through a complete and updated documentation and an active support team. Technical issues may arise, and you want the support team to be available to address the issues to minimize disruption. Consider asking the support team how they handle technical issues before subscribing to the platform.

Conclusion

AI is revolutionizing the way we do things, and your business should get on board as soon as possible. The endless possibilities of AI are making industries smarter: from agriculture to medicine, sports, and more. Data annotation is the first step towards innovation.

Now that you know what data labeling is, how it works, its best practices, and what to look for when choosing a data labeling platform, you can make informed decisions for your business and take your operations to the next level. Are you ready to get started?

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