Automate Data Labeling: What is It, and How Can Implementing It Help?


Labeling data is crucial when developing Machine Learning models. To train the model and determine its accuracy, it is necessary to assign labels to each data point. However, classifying data by hand can be tedious and time-consuming, especially for large datasets. This is where computer-assisted data labeling comes in.

Automation of data labeling is referred to as “automated data labeling,” It occurs when computers are employed to assign categories to data sets. Simply described, it’s the action of feeding information into a Machine Learning model. The model then assigns appropriate labels to each data piece. Manually labeling data can help reduce time and effort while improving the labels’ accuracy and consistency.

Implementing automatic data labeling in Machine Learning applications has various advantages:

  • Automated data labeling can significantly minimize the time and effort necessary for manual data labeling, resulting in increased efficiency and speed. This is especially significant for huge datasets where hand labeling would take an unacceptable amount of time. Automating data labeling permits the building Machine Learning models at a significantly faster rate.
  • Automated data labeling can also reduce the expenses associated with manual data labeling. Because the procedure is automated, it takes more human resources than manual data labeling. This can result in cost savings for businesses that utilize Machine Learning.
  • Increased precision and consistency: The possibility of human error is one of the primary obstacles to manual data labeling. Automated data labeling can avoid this danger by consistently and precisely adding labels. This can increase the overall precision of the Machine Learning model and lead to improved outcomes.

Automated data labeling eliminates time-consuming and costly factors. However, it is difficult to determine what can and should be automated. Utilizing smoother interfaces, model-assisted pre-labeling, and active learning, among others, have been created to make the labeling process more efficient and user-friendly. However, the fundamental restrictions remain, as the input remains unchanged: individual labels are collected sequentially without explanation.

Limitations of Manual Data Labeling

Manually labeling data is an essential step in the Machine Learning process, but it can be hard because it can cause bottlenecks and slowdowns. It takes a lot of time and works for a large team of annotators to find and label the important parts of each image. Managing a group of labelers also ensures that the labeling process is consistent since differences can throw off the model. Also, it’s expensive to hire a team of data labelers to work in-house, and outsourcing can cause problems with communication and accuracy.

Privacy and lack of knowledge in a certain area can also limit who can help with manual labeling. People from the crowd can do some tasks, but most tasks important to an enterprise require specific domain knowledge. Also, companies are often reluctant to share their internal data with outside sources. This means there may not be enough qualified people to label data for a certain task, making the bottleneck even worse.

Many of these problems can be solved by automating the process of putting labels on data. Even though humans are still needed, automation can reduce the manual work required, cut costs, reduce mistakes, and speed up the whole process. By adding automation to the workflow, Machine Learning professionals can get around the bottleneck that has been a problem since the beginning of artificial intelligence.

Let’s discuss different types of automated data labeling

Model Assisted Data Labeling

Model-assisted data labeling is a method for labeling data by hand that uses Machine Learning models to help. The idea is to use the power of models to pre-label a certain part of the data, reducing the amount of work that must be done by hand. The pre-labeled data can then be looked at and changed by humans, who can label the data they haven’t seen yet.

Model-assisted data labeling can be done in many different ways, depending on the data type, the task, and how well the model works. Some examples include:

  • Active learning: A small data set is used to train a model that uses active learning. It is then used to choose a subset of the remaining data that is most uncertain or gives the most information about how the model is doing. Humans label this subset, and the model is updated to include the new labels. This process is repeated until the model works well enough or until the budget or time runs out.
  • Transfer learning is when a model that has already been trained is fine-tuned on a small labeled dataset and then used to predict labels on a larger dataset. Human annotators look over the predicted labels and fix them, and the model gets even better as more data is labeled.
  • Annotation prediction: A model is trained to predict the annotation of a data point (such as a bounding box, a segmentation mask, or a class label) based on the data point and its surroundings (e.g., surrounding data points, meta-data). Human annotators look over the predicted annotations and make changes to them. The model is then updated with the new data that has been labeled.

Data labeling with the help of a model can be done more quickly, with fewer errors, greater consistency, and hence better model performance. It also needs Machine Learning, data annotation experts, a well-thought-out annotation process, and regular checks on the model’s accuracy and bias.

Programmatic labeling

Automatic data labeling, or “programmatic labeling,” relies on pre-existing software and algorithms to assign labels to data. Semi-automated data labeling is a synonym for automated data labeling. In comparison to manual labeling, programmatic labeling has the potential to cut down on time and money spent on labeling drastically.

Some examples of programmatic labeling methods include:

  • Rule-based labeling: Rules, such as regular expressions or image segmentation algorithms, are used to generate the labels.
  • Clustering-based labeling: Data points with shared traits or properties are clustered together to create labels.
  • Supervised learning-based labeling: When applied to unlabeled data, the results of a model’s training on a labeled dataset provide the labels.

Utilizing programmatic labeling has several benefits, such as:

  • Compared to manual labeling, the data labeling process can be completed much more quickly with programmatic labeling.
  • Consistency: Errors and inconsistencies in the labeling process can be minimized through programmatic labeling.
  • Large datasets, which would be impractical or impossible to classify manually, are suitable for programmatic labeling.
  • Cost savings can be realized through programmatic labeling because of the decreased need for human labor.

While there are benefits to utilizing programmatic labeling, there are also drawbacks.

  • Knowledge of the data and the goal and programming and Machine Learning expertise are prerequisites for programmatic labeling.
  • The limitations of programmatic labeling include the inability to accommodate more complicated or open-ended tasks due to the use of pre-defined rules or models.
  • Automated labeling techniques may not work well with specific data types, such as images with complicated backgrounds or films with several moving objects.

It is important to keep in mind that automated data labeling has its drawbacks:

  • Critical need for high-quality training data: For accurate label predictions to be made by automated data labeling algorithms, good-quality training data is essential. Labels assigned by an automated system can be more reliable if the training data is comprehensive and high-quality.
  • Implied bias: Auto-labeling has its advantages, but it also comes with the potential drawback of bias. The accuracy of the labels produced by an automated data labeling system might be compromised by the inherent bias of the training data used to create the system. To overcome this problem, it is crucial that the training data used to construct the system be both representative and objective.

Although these drawbacks exist, automated data labeling remains a useful method for enhancing the speed and precision of data labeling in Machine Learning applications. By automating this step, organizations can save time and money, boost model accuracy, and streamline processes.

However, not all data annotation tasks can be automated. It is unrealistic and impossible to automate data labeling without human input fully. Due to the superior domain knowledge of humans. they play a crucial part in the process.

Improving the level of abstraction at which data scientists and domain specialists label their data is critical for the success of automated data labeling. This places humans at the heart of the labeling process while also marginalizing them. Labeling functions that record the reasoning behind the labels are one example of higher-level inputs that are used to transmit domain knowledge.

Conclusion and Final Thoughts

Labeling strategies for Machine Learning projects vary by task and development stage. For example, while automating the process of establishing a ground truth could appear convenient at first, it could end up being counterproductive because it produces an erroneous model. Manual labor isn’t ideal for sophisticated segmentation jobs, but it could be fine for bounding boxes or other similarly straightforward tasks.

In addition to improving the quality of the labeled data, automated data labeling can save valuable time and resources for businesses. Automatic data labeling can be a helpful addition to any Machine Learning workflow, albeit it is important to review the labels once they have been applied.