in Blog

February 09, 2023

What is data labeling in machine learning?

Author:




Edwin Lisowski

CSO & Co-Founder


Reading time:




11 minutes


The world generates upwards of 2.5 quintillion bytes of data every day. [1] Unfortunately, most of this data isn’t leveraged for business growth despite the value it offers to big data analytics, natural language processing, and machine learning applications. For a business to create an effective machine learning model, it needs to collect, store, and label the data so the model can make sense of it. With data collection strategies and cloud storage capabilities at their peak, businesses are left to contend with the most intricate processes of the three – data labeling.

This article will provide a detailed guide on data labeling for machine learning models, focusing primarily on types of data labeling, data labeling tools, and the best practices for labeling data for machine learning applications.

What is data labeling? Definition

Also known as data annotation, data labeling is the process of identifying raw data and adding meaningful labels, so machine learning models can understand its context and learn from it. [2]

For instance, if you plan to create an image-based machine learning model, you have to include labels on all photos to indicate the context of the photograph, i.e., whether it contains a person, dog, car, etc. This applies to all forms of data, including images, text files, and videos.

Data labeling definiton and meaning

How does data labeling work?

Most machine learning models today utilize supervised learning. This typically involves using an algorithm to match input to a desired output. For this to work, you need a set of labeled data from which the machine learning model can learn.

The data annotation process typically involves a group of human levelers who make judgments on different pieces of unlabeled data. For instance, a company may contract a group of human labelers to tag all images in a data set that contains a moving vehicle. The labeling process can be as simple as yes/no choices or more complex as marking out all pixels in the image associated with a moving vehicle.

Here is a simplified walkthrough of the specific processes involved in data labeling:

Data Labeling Processes

Data labeling typically involves:

  • Data collection
  • Data tagging
  • Quality assurance
  • Model testing and training

data labeling processes: main steps

Data collection

Before you even think of labeling data, you first have to collect a vast amount of relevant data that meets the requirements of your machine learning model. You can collect data in several ways, including:

Manual Data Collection

Manual data collection is only feasible in cases where more automated forms of data collection aren’t possible. The process typically starts with defining the type of data to be collected, developing data collection instruments such as CRM systems, entering the data, then validating it. The entire process is time-consuming and labor-intensive, prompting businesses to use other methods where possible.

Open-Source Data Sets

Using open-source data sets presents a cost-effective means of data collection. The easy accessibility of open-source data makes this method particularly suitable for small businesses without large data reserves.

Unfortunately, open-source data is prone to numerous vulnerabilities including the potential for gaps and inaccurate data, which can vastly affect the performance of a machine-learning model. Therefore, organizations seeking this mode of data collection need a reliable source with validated data.

Synthetic Data Generation

Synthetic data generation typically involves using simulators (computer programs) that closely mimic real-world data in terms of distribution, patterns, and relationships. The biggest selling point of synthetic data generation is the level of scalability and convenience it provides.

Data tagging

Once you have your raw data ready, you need human labelers to identify the elements in the data using a data labeling platform. Due to security concerns, most organizations choose to do this in-house since they don’t want to share sensitive information with third parties. [3]

Quality assurance

Before applying collected data to your machine learning model, you must verify the data’s accuracy and quality. Your goal here is basically to ensure that the data is accurate, relevant, and free from errors so that the machine learning models can perform effectively and produce accurate predictions. This process typically involves:

  • Data cleaning
  • Data labeling
  • Data validation
  • Data augmentation
  • Data balancing

quality assurance process

Model testing and training

Once you’re sure of the quality of your data, you need to incorporate it into your machine-learning model and test it. The best and most effective way to test a model is by exposing it to unlabeled data, then testing the accuracy of its predictions. This way, you can get an estimate of the model’s success rate and either deploy or re-train it.

Data labeling types

There are three types of data labeling. They include:

Computer vision

This is a branch of AI that enables computers to recognize and derive meaningful information from images. [4] When building a machine learning model for a computer vision system, you first need to label the images correctly. This typically involves labeling the images themselves, key points in the images, or creating borders around specific objects in the image and then labeling them.

For instance, you can classify images by quality and content or segment the images at the pixel level to identify objects within specified borders. Once labeled, these images can be used as training data to build a model that can automatically categorize images, detect objects in the images, and identify key points in the image or segment images.

Read more about Training Data for Computer Vision

Natural language processing

Natural language processing is an AI application that gives computers the ability to ‘see’ and understand human-generated text and speech. [5] In NLP data labeling, you first have to manually identify relevant sections of a text or audio file, then add specific labels to create a data set of training data.

This may involve anything from identifying the sentiment behind an audio or text blurb, classifying proper nouns, and identifying parts of speech to identifying text in images. To achieve this, you must draw borders manually on a given text or time-stamped audio file and then transcribe its contents into your data set.

Audio processing

Audio processing typically involves transforming different types of sound into a structured format that can be used in ML applications. The process generally involves transcribing the sounds into written text, then adding relevant tags to categorize the audio. Audio ML models use the labeled data sets as training data.

Data labeling tools

Data labeling tools are software designed to label raw data in various formats like text, images, and audio formats to train machine learning models. These tools often come with a user-friendly interface where human labelers can view the raw data and add labels.

According to a McKinsey report, data labeling is one of the most challenging aspects of training an ML model. [6] Data labeling tools can help simplify this process and generate high-quality data necessary to effectively train ML models.

Most large organizations with the necessary resources create their data labeling tools in-house. However, due to this approach’s time-wastage and cost implications, most small businesses opt for off-the-shelf software solutions.

Some of the software solutions are available as free packages, but the most advanced ones come as paid packages. The main difference between the two choices is their effectiveness and applicability. Most free software solutions only offer basic labeling instruments, which may not be sufficient to label complex data sets. Premium software solutions, on the other hand, offer additional customization options and APIs.

Some of the best data labeling tools on the market include:

Label Studio

Label studio is a web application that offers data labeling and exploration services for different types of data, including text, image, and audio files. It has a python-base backend and a React and MST front end. This unique feature supports all browsers and can be incorporated into different applications.

label studio platform, dashboardSource: labelstud.io

The streamlined UI, along with its multi-data support capabilities, makes it suitable for all ML applications, and the end results (labeled data sets) are pretty accurate, too.

Sloth

Sloth is an open-source data labeling program created specifically for handling computer vision data annotation applications. You can use the tool as a framework or a collection of standard components that can be combined effortlessly to meet your data labeling requirements.

Sloth is relatively easy to use. It gives you control over all features and capabilities, including custom features and predefined presets, making the data labeling process much easier.

Tagtog

Tagtog is a text-based data annotation tool specifically designed for handling text formats. It has a pretty user-friendly user interface that allows you to label data and manage the labeling process through integrated features that further enhance the processing speed.

pdf annotation, data labeling tool, tagtog dashboardSource: tagtog.com

Audino

Audino is an open-source audio annotation program. The program comes with a key-based API that enables you to upload and assign data to multiple users. This feature makes it perfect for handling huge data annotation tasks that require multiple human labelers.

This audio annotation program also offers extensive flexibility. It enables you to perform various tasks such as speaker identification, speech recognition, characterization, and voice activity detection. Unfortunately, its numerous features and complex UI may make it difficult for beginners to use effectively.

The best practices for labeling data

Collect diverse data

One of the biggest challenges facing ML models and other AI applications is bias. To limit the possibility of bias in your ML model, you need to diversify your training data as much as possible.

For instance, if you’re collecting data for a predictive model for law enforcement, you can limit the possibility of bias towards a certain minority by taking arrest data from different locations, not just where the minorities live.

The same applies to training models for autonomous vehicles. In order to be effective, their training data has to come from numerous road types to enable them to navigate different terrain and traffic conditions.

Only collect data specific to your project

Machine Learning models are only as good as their training data. To be effective, they need specific data that are relevant to their intended purpose. Feeding an ML model with disparate data will inevitably cause ‘confusion’ within the system, thus affecting its accuracy and effectiveness.

Measure your model’s performance

The performance of ML models is dependent on the size of tier training data. ML models with larger training data sets generally perform better than their smaller counterparts. Most organizations use a somewhat limited sample of training data, then add more with time to improve the model’s performance.

Each addition subsequently improves the model’s performance until it reaches a point where subsequent improvements become too menial. At this point, you may give up and choose to deploy the model as-is. But for greater effectiveness, it is advisable to try and figure out what’s causing the performance bottlenecks through human-in-the-loop (HITL) fine-tuning [7].

While you’re at it, you may discover that you need to change your model or approach. This might include anything from improving the quality of your data sets to changing them altogether.

Leverage external data labeling services

Data annotation is a labor-intensive and time-consuming process that could take up a majority of your in-house IT team’s time. It is also quite expensive. There are numerous companies that specialize in data labeling tasks. These companies could accomplish your data annotation goals faster and more effectively as well as cheaper than a relatively inexperienced in-house team.

With that said, there’s also the issue of data security for sensitive projects. In case you’re dealing with a sensitive project, it would be better to handle it in-house or look for a reputable company with a proven track record of maintaining their clients’ privacy.

Final thoughts

Data labeling is one of the most intricate aspects of training an ML model. Everything from the quality of your data to how you annotate it directly impacts the subsequent performance and accuracy of your model.

Therefore, it is vital to only use high-quality data coupled with the right data annotation tools. Fortunately, there are numerous open-source and premium data annotation tools available on the market that are designed to handle all sorts of data annotation operations. See our MLOps consulting to find out more.

Also check out our machine learning consulting services to learn more.

References

[1] Forbes.com. How Much Data do we Create every day. URL: https://bit.ly/3Iaf0aM. Accessed February 6, 2023
[2] Ibm.com. Data Labeling. URL: https://www.ibm.com/topics/data-labeling. Accessed February 6, 2023
[3] Scsonline. Georgetown.edu. Top Threats to Information Technology. URL:  https://bit.ly/3lm5EQz. Accessed February 6, 2023
[4] Wgu.edu. Computer Vision Applications guide. URL: https://www.wgu.edu/blog/computer-vision-applications-guide2111.html. Accessed February 6, 2023
[5] Se-education. org. Natural Language. URL: https://bit.ly/3HPPExJ. Accessed February 6, 2023
[6] Mckinsey.com. What AI Can and Can’t Do Yet For Business. URL:  https://mck.co/3jKbfj3.
[7] Link.springer.com. URL: https://link.springer.com/article/10.1007/s10462-022-10246-w. Accessed February 6, 2023



Category:


Machine Learning