Ever wondered how machines learn from the data we feed them? It’s not a simple case of writing lengthy instructions and overloading information. Machines need data that’s prepped and presented in a comprehensive way. Data labeling is the secret to unlocking the true potential of data for machine learning.

Data labeling tools help businesses turn unlabeled data into labeled data to build corresponding AI and ML algorithms for their needs. Properly labeled or annotated data forms the basis of a model’s understanding so it can apply learned knowledge to new, unlabeled data.

Why use data labeling?

Given the critical role of data in AI, labeling guarantees that training data and testing are structured meaningfully for the intended applications. Data labeling is critical in supervised learning as it allows a machine learning model to learn and make predictions based on data structure and patterns. 

High-quality labeled data results in precise and accurate machine learning models. On the other hand, if the data label is incorrect, the model’s output will likely also be inaccurate. It will struggle to perform its intended task effectively.

Data labeling also fosters a deep understanding of data. The process involves careful examination and categorization of data points, which can often reveal an organization’s hidden patterns and insights that may not be apparent at first glance. 

This deeper understanding supports various applications, such as improving existing machine learning models, identifying new business opportunities, or simply gaining a better grasp of the information you possess.

Labeled data vs. unlabeled data 

Labeled data refers to datasets tagged with one or more labels to identify specific properties or characteristics. Machine learning models use these datasets to educate themselves during the training process. The labels act as a guide to help the model understand the data and make predictions or classifications relevant to the task.

Unlabeled data refers to raw datasets, which means they exist without any accompanying labels or explanations. Machine learning models use this data type for unsupervised learning, wherein the model tries to identify patterns and structures within the data without any prior guidance about what outcomes should be predicted.

Feature	Labeled Data	Unlabeled Data
Definition	Data with specific labels	Data without any labels
Example	An image labeled as “cat”	An image with no label
Application	Supervised learning	Unsupervised learning
Advantages	Faster training	Abundant and readily available
Disadvantages	Expensive and time-consuming	Requires additional processing to extract meaning

How does data labeling work?

The process of data labeling involves a series of steps that often include human annotators and machine algorithms to assign meaningful labels to different kinds of information.

• Data collection: The first step is gathering raw data from which the model will learn. This data can include images, videos, text, audio, or any other relevant format for any given task.
• Data preparation: Once collected, the data may need some cleaning and pre-processing to remove irrelevant information, format the datasets for consistency, or split large sets into smaller ones for easier handling.
• Labeling process: Once the data is prepped, human labelers or annotators come into play. They analyze the prepared data and add specific labels that describe the content perfectly.
• Quality assurance: To verify the accuracy and consistency of the labeled data, a quality assurance process begins. During this step, annotators review different subsets of labeled data to identify and rectify discrepancies.
• Model training: Finally, the labeled data is fed to the machine learning model. The model learns to identify patterns and relationships between the data and the corresponding labels by analyzing the labeled examples. This learning process empowers the model to predict new, unseen data accurately.

Common types of data labeling

Different types of data labeling are used depending on the nature of the data and the problem at hand. Here are some common types. 

Computer vision

Computer vision pares down the process of assigning meaningful labels to various objects, scenes, or actions within visual data. One common application is image classification, whereby computer vision algorithms automatically categorize images into predefined classes. For instance, in a dataset of animal images, a computer vision model can be trained to recognize and label images of cats, dogs, or birds. 

Another critical aspect is object detection, which sees computer vision identifying and outlining specific objects within an image using bounding boxes. This is particularly useful for scenarios where multiple objects coexist in an image, such as detecting and labeling different vehicles on a road. 

Another computer vision technique is semantic segmentation. It involves labeling each pixel in an image with a corresponding class to provide a detailed understanding of object boundaries and segmentation. These computer vision approaches significantly accelerate the data labeling process and reduce the manual effort required for annotating large datasets. 

Computer vision facilitates the creation of more granular and precise annotations, which enhances the quality of labeled datasets. It enables applications like facial recognition so computer vision automatically detects and labels faces in images or videos. It enhances efficiency and contributes to the accuracy and scalability of machine learning models.

Natural language processing (NLP)

NLP involves identifying and classifying attributes such as names, locations, and organizations within text. NLP models assist annotators by automating parts of this process. Sentiment analysis, another NLP application, helps with labeling text with sentiments like positive, negative, or neutral, expediting the annotation of emotions or opinions in large datasets. It’s essential to initially segment and annotate sections of text with relevant tags within your dataset. 

For instance, this process might comprise marking the underlying sentiment or purpose behind a section of text, pinpointing various parts of speech, classifying locations and personal names, or highlighting text embedded within images. By using NLP technologies, data labeling in the realm of natural language becomes more efficient, accurate, and scalable, ultimately supporting the training of robust machine learning models for chatbots, language translation, and sentiment analysis.

Audio processing 

Audio processing techniques convert spoken words into written text to facilitate labeling oral content. It transforms an array of sounds, ranging from human speech to nature sounds like animal calls, into a structured format suitable for machine learning applications. 

The initial step in this process typically consists of transcribing the audio content into text format. The data can then be enriched with labels and classified into categories for deeper analysis and understanding of the audio’s characteristics. 

This labeled and categorized dataset serves as the foundational training material for machine learning algorithms that target audio-based tasks. It refines the data labeling process for audio datasets to support the training of models for applications such as speech recognition, speaker identification, and audio event detection.

Key approaches to data labeling 

Organizations apply data labeling using different approaches depending on the scale of the dataset, quality standards, and resource availability. Here are key approaches to data labeling.

• Internal labeling: The internal labeling approach requires more time and effort since it uses in-house data science experts. It typically favors large companies with comprehensive resources but does provide greater accuracy, simplify tracking, and increase quality.
• Synthetic labeling: Synthetic data labeling generates new project data from pre-existing data sets. It does so by using extensive computing power – in turn increasing the pricing. This method enhances data quality and time efficiency.
• Programmatic labeling: This approach uses scripts that reduce time consumption and human annotation. Due to the possibility of technical problems, human-in-the-loop (HITL) is part of the quality assurance process.
• Crowdsourcing: Crowdsourcing platforms, like Amazon Mechanical Turk or Figure Eight (formerly CrowdFlower), allow you to distribute your data labeling tasks to a large online workforce. This approach can be faster and less expensive than in-house labeling, though the quality of the labels may vary due to the range of skill levels of the crowd workers. 

Benefits of data labeling 

In today’s tech-driven world, investing in data labeling is a smart move for any business that uses machine learning. Some key advantages of implementing the data labeling process are discussed here.

• Optimizing model variables: Data labeling simplifies complex categorical variables into binaries that are easier for the model to understand. It also helps include control variables, which are essential for interpreting the relationships between different variables of a model. This optimization reduces the complexity of the model and improves its efficiency.
• Improved prediction accuracy: Data labeling ensures that the model learns from the right information to reduce the chances of mistakes or biases creeping in. This keeps the model’s results reliable and trustworthy. It also helps the models understand the data better and learn patterns more effectively, leading to improved performance in tasks such as recognizing objects in images or understanding language.
• Data usability: Consistent data labeling improves the usability and accessibility of labeled datasets for tasks such as model evaluation, validation, and deployment. Well-labeled datasets are easier to interpret, analyze, and visualize, and they enable stakeholders to derive actionable insights and make informed decisions based on the data.
• Consistency: Consistent data labeling improves workflow and makes it easier and faster for annotators to label large volumes of data. Well-defined labeling guidelines and standards help maintain consistency among different annotators and labeling tasks, which reduces the time and effort required for data preparation. 

Challenges of data labeling 

While there are benefits to data labeling, it also presents challenges. Some of the most common are:

• Dealing with the high costs: Training machine learning models requires data labeling, but it can strain resources and budgets. As datasets get bigger, labeling them becomes more expensive and time-consuming. Whether using professional annotators or services, finding the right balance between cost and quality is challenging.
• Tackling time constraints: Even with some automation, setting up data pipelines and manually labeling large amounts of data remains a lengthy process. Developing efficient labeling strategies and tools saves time.
• Reducing human errors: Humans can make mistakes when labeling data, lowering its quality and affecting model performance. Implementing quality checks catches and corrects errors early on.
• Addressing privacy and security concerns: Labeling sensitive data raises privacy and security issues. Ensuring compliance with regulations and implementing strong security measures protects labeled data from unauthorized access.

Confirming accuracy and efficiency in data labeling is crucial for training robust machine learning models and achieving desired outcomes. 

Here are some best practices to consider, regardless of your approach.

• Proper data collection: In ML, one of the key factors to consider is the quality of the data. The data has to be varied, yet closely aligned with the problem at hand. Diversity in the data allows for the development of models equipped to handle different scenarios, while specificity reduces the likelihood of errors.
• Conduct bias checks: Perform thorough bias checks to prevent your model from becoming overly specialized in one particular situation. This creates a model that adapts across a range of circumstances.
• Use a proper annotation approach: Establish clear guidelines for annotators, provide training, and implement quality control. Regularly update guidelines for efficiency and adaptability. This approach creates reliable datasets for machine learning and insights.
• Establish a QA process: Start with clear guidelines, criteria for evaluation, and training. Assign experienced reviewers to assess labeled data in order to provide feedback. Iterate based on insights and consider automation tools. Maintain open communication for feedback among teams. 

Best data labeling software for 2024

Data labeling solutions are critical for companies that work with machine learning. These tools enable the creation of high-quality labeled data, which is useful for developing accurate and robust machine learning models.

To qualify for inclusion in the Data Labeling category, a product must:

• Integrate a managed workforce and/or data labeling service
• Ensure labels are accurate and consistent
• Give the user the ability to view analytics that monitor the accuracy and/or speed of labeling
• Allow the annotated data to be integrated into data science and machine learning platforms to build machine learning models

Below are the top five leading data labeling software solutions from G2’s Spring 2024 Grid® Report. Some reviews may be edited for clarity.*

1. SuperAnnotate

SuperAnnotate is a leading platform that lets you build, fine-tune, and iterate AI models with high-quality training data. The platform facilitates collaboration among team members and offers management tools that keep track of project progress, data curation, and automation features. It’s designed to support a secure and efficient workflow, whether for small teams or large enterprises working on multiple and challenging datasets.

What users like best:

“The platform allows users to organize datasets, assign tasks to team members, track progress, and monitor annotation quality effortlessly. The ability to create custom workflows and automation rules further enhances productivity, enabling teams to efficiently handle large-scale annotation projects.”

– SuperAnnotate Review, Hoang D. 

What users dislike: 

“ Finding results based on a specific condition is still code-based. That’s one thing I found where it could use some improvement.”

– SuperAnnotate Review, Sai Bharadwaj A. 

2. Encord

A leading data annotation and active learning platform, Encord provides tools for teams working with visual data. It’s an end-to-end platform that helps where you can safely develop, test, and deploy AI systems at scale. Use it to create high-quality training data, fine tune models, and assess quality. 

What users like best:

“I like the ability of task management and automation tools to simplify and optimize complex workflows. Such tools can help increase efficiency and productivity, reduce errors and redundancies, and enable better collaboration among team members. The convenience of having everything organized and tracked in one place also adds to their appeal.”

– Encord Review, Alve H. 

What users dislike: 

“The tool could benefit from some customization options. The ability to personalize hotkeys and tool settings according to user preference would greatly enhance the user experience.“

– Encord Review, Samuel A. 

3. Kili

Kili Technology is a comprehensive annotation tool used to label training data, fix issues in data labeling processes, and simplify the labeling process. It comes with customizable interfaces, powerful workflows for accuracy, and automation tools. It helps you focus on reviewing data that matters, quantify quality with insights, and import and extract data efficiently. 

What users like best:

“One of the fantastic aspects of this platform are the quality monitoring features, which make it easier to ensure that the labeled data is accurate and reliable. Additionally, the collaboration features are useful, because it allows team members to work together, sharing progress and ensuring efficiency.

Very intuitive user-friendly interface too, making the labeling process seamless and easy to understand.“

– Kili Review, Evi T. 

What users dislike: 

“The API is too complex to use if you don’t have technical knowledge like me. I couldn’t set up more advanced quality workflows. I managed to set up the standard ones.”

– Kili Review, Melly A. 

4. Dataloop

Dataloop is a platform designed for data annotation, model development, and data management. It’s predominantly used in AI and machine learning contexts, especially when dealing with large datasets and images. It’s transforming the way organizations build and use AI applications.

What users like best:

“Dataloop has been a valuable asset in streamlining administrative tasks for my colleagues and myself by efficiently organizing management and numerical data. It functions as a convenient tool that keeps important information easily accessible, improving our work’s organization and speed by providing in-depth insights into our job’s operations.”

– Dataloop Review, Deepak G. 

What users dislike: 

“It took me some time to figure out the flow of the program and it would be helpful if there were tutorials available to guide users. The setup process also took longer than expected, but this may vary depending on the vendor.”

– Dataloop Review, Yogendra S. 

5. V7

A powerful AI training data platform, V7  helps annotate images, documents, medical imaging files, and videos. With high quality assurance and team collaboration tools, V7 assists saves time and increases accuracy. 

What users like best:

“After several tries with various tools to annotate my data, I stumbled on V7 and immediately realized that V7 had exactly what I needed. My datasets have a lot of similar images and V7’s copy annotations feature helps save a ton of time and allows me to work through my datasets swiftly. Furthermore, I never knew I needed the image manipulation options that V7 provides until I used it. It allowed me to completely isolate my items from the noise for more accurate annotations. Also, V7’s UI looks amazing and is incredibly simple to use. There’s no learning curve.“

– V7 Review, Suneth T. 

What users dislike: 

“V7’s occasional tendency to lag while working with large datasets increases the amount of time you spend on the assignment.“

– V7 Review, Desmond A.

Turning chaos into clarity 

Raw data alone isn’t enough to unlock its true potential. Data labeling plays a crucial role in the development and advancement of new technologies, particularly in machine learning and artificial intelligence.

By properly labeling data and following best practices, organizations can open up new opportunities and move toward a future where decisions are driven by data.

Optimize your data infrastructure with data modeling to refine AI and ML models with labeled data.