2022-03-10 10:00

Our Guide to Data Labeling in Machine Learning

Our Guide to Data Labeling in Machine Learning

Data labeling is the task of systematically recognizing and identifying specific objects within raw digital data, such as video stills or computerized images (in the context of computer vision), thereby “tagging” them with digital labels that enable machine learning (ML) models to create accurate forecasts and assessments.

Here is some further reading material on other forms of data audio transcriptiontext annotation in a document. These pertain more to the context of natural language processing.

Data labeling and data annotation techniques are characteristic of the initialization stage when developing a machine learning model. It requires the identification of raw data, and then adding one or more labels to that data, to specify its context for modeling. This preprocessing stage permits the machine learning model to assemble accurate predictions.

For example, digital data annotation can permit autonomous automobiles to stop when approaching pedestrian crossings, digital assistants can recognize certain human voices, and digital security cameras can notice and witness strange or suspicious conduct.

Machine Learning has made significant advances within the last twenty years or so. This has been attributed to concurrent and contemporary improvements in computing processing power and the major breakthroughs achieved in deep learning research. Another key catalyst has been the quantity of digital data that has been collected and stockpiled. These advanced algorithms have progressed rapidly, and their need for digital training data has kept pace too.

A key concept required for successful machine learning outcomes is the technique called supervised learning. Supervised learning is a machine learning approach defined by utilizing labeled datasets. These datasets are designed, over time, to train or “supervise” algorithms to correctly classify data, thereby predicting accurate outcomes. The machine learning model can measure its accuracy by processing the datasets and, therefore, self-learn over time.

How Precisely Does Data Labeling Work?
image annotation of animal

1. Data Collection Process

The collection of a significant amount of digital data – images, video stills, videos, audio files, transcribed texts, etc. – is typically the first step in the process. A large and mixed quantity of digital data will guarantee more accurate computerized results in the long run than a diminutive amount of digital data can provide.

2. Data Labeling

Digital data tagging consists of human enforced data labelers who identify those entities amongst the unlabeled data while operating on a data labeling training platform. For example, human data labelers might be tasked with demarcating whether a digital image comprises people (or not) or how to track a tennis ball during a tennis match within a video.

3. Quality Assurance Methods

The labeled or annotated data must be instructive and accurate to nurture high-performing ML, business models. Therefore, an internal and trusted quality assurance process is mandated to review the accuracy of the digitally labeled data. Otherwise, there will be a high risk that the ML business model will fail to perform satisfactorily.

What Factors Affect the Quality of Data Labeling?data labeling of image houses

1. Information and Context

With data labeling, there is a key requirement for basic domain knowledge and contextual understanding within the labor workforce. These skills are mandatory to be able to construct high-quality, structured digital datasets for ML business models. Data labelers will label their data with a higher level of quality when they understand the reasoning and context of the data they are digitally annotating.

You can learn more about datasets through these useful links: How to create a dataset for Machine LearningHow to clean a dataset in python

2. Agile yet Inflexible

ML business modeling is an iterative process. Data labeling will naturally evolve as the ML business models' constructive testing and quality validation develop, and key learnings are adopted from their outcomes.

Digital data labeling units must have the ability to be flexible to merge changes that adjust to meet the end users' changing needs or products. A robust, yet flexible, digital data labeling team should be able to react positively to data volume changes, increases with task complexity, and task duration shifts. The more adaptive the labeling team can be, the more successful the ML business projects will be.

3. Communication is Key

Positive and direct communication within the digital data labeling team is vital. Any practical and closed feedback loop is an immaculate means to establish dedicated communication and cooperation between the ML project team and the digital data labelers. In addition, data labelers are always advised to share their key learnings as they digitally label the data, so these valuable insights can be adapted to modify the approach if required.

Some Common (and Important) Missteps to Avoid During The Data Labeling Processdata labeling using bounding boxes

Once a data labeling team is established, performs its duties, and commences delivering valuable results, it can easily get carried away when deep diving into data labeling for any ML business project. Instead, one needs to be mindful of the missteps and pitfalls that may derail any ML project – and certain principles or rules must be adhered to when growing the digital data labeling enterprise.

1. Punctuation Issues, Excess Whitespace, and Case Sensitivity Rules

When performing data labeling, it is vital to ensure that the tag finalization process is merely a continuation of the format process that has been created  – and agreed upon – beforehand. Any new tags that are required to be added must be previously communicated to the entire team. When annotating text, for example, whitespace and other punctuation issues can be problematic. Such differences can generally be rectified using an assortment of computer scripting tools that are available on the market.

Any digital labeling exercise will encounter various challenges to overcome along the data annotation journey. However, occasionally some challenges that are encountered are a symptom of a more intrinsic issue that needs to be dealt with. For example, within the medical imaging industry, there is a highly-skilled requirement for the data labeling workforce to have a base level of medical knowledge and practical experience when viewing intricate images resulting from X-rays, MRI, and CT scans.

2. Nested and Inverted Data Tags

Further issues may arise when dealing with the scenario of inverted or nested data tagging. Let’s use a text document as an example: an initial tagged sentence or phrase may be correct since the digital tags were used according to the order in which they appeared within the sentence. But what about the scenario where the exact same words or phrases occur but in a different order, yet still with the same meaning? We could end up with a totally different order of digital tags, which therefore will attach a totally different meaning to what are essentially the same words, but in a different order. When using nested labels, all of the resources that are digitally labeling the ML dataset must operate in the same format. This issue can be extrapolated to other data types, such as images and videos.

3. Adding Any New Labels to Your Project Mid-Term

Halfway through a digital labeling undertaking, your team may realize that there will be a need for a new digital label that is currently not present in the agreed-upon master labeling list. In one initial undertaking, the team might simply add a new required data label to the master list and thereby inform the whole team to now use this new digital label when required.

But this process should be highly discouraged, and the simple reason is that the labeled data the team has already marked would have to be re-examined to see if the new label suddenly fits into the already label-processed digital data. It is best practice to always compile and agree upon the list of digital labels before commencing any labeling endeavor. If a new data label is devised as a required mid-project, it is ideal to take note of this occurrence and then review – as a team – how the data label was missed after the data labeling exercise is completed, and how this discovery can be prevented from re-occurring beforehand.

Data Labeling Best Practices

Acquiring high-quality digitally labeled data is an expansion barrier that becomes more significant when contemplating the handling of complex ML models. Yet, there are many strategies and approaches to improve the efficacy and precision of the data labeling process:

  • Clear Labeling Instructions: Communication with the digital data labelers and providing clear labeling requirements will ensure the desired accuracy of the delivered results.

  • Label Verification: It is necessary to audit the digital labels to confirm their exactness and make any adjustments if required.

  • Learning Transferration: Another method to improve the efficacy of data labeling is to re-use previous data labeling assignments to create hierarchical data labels. Essentially, ML operators can utilize the output of previously digitally-trained ML project models as input for another new project.


AI is revolutionizing the course on how we perform certain repetitive functions, and enterprises that have adopted such business models are gaining the benefits. The technological opportunities AI can deliver are potentially inexhaustible and will assist diverse industries to become smarter: from medicine to agriculture, to recreation and sports. Data digital annotation is the first stage towards such innovation.

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