Drive Smart AI/ML Projects with Data Annotation and Labeling

Efficiency drives business growth by maximizing the utilization of resources. One of those valuable resources in today’s world is data. Every department generates and consumes large quantities of it, regardless of the industry a business operates in. Thus, maintaining data management efficiency and accuracy is of paramount importance. And there are no better tools for it than Artificial Intelligence and Machine Learning since they not only perform the computational operations of regular computing but also improve on it. Such an AI/ML model is achieved through the processes of data labeling and annotation.

The advantages offered by these technologies have made them the mainstay of businesses worldwide, with related tools gaining investments to the tune of US$ 629.5 Million in 2021. And that is expected to grow at a rate of 26.6% between 2022 to 2030. Thus, it is not far-fetched to say that Data labeling and annotation will determine the fate of a business going ahead, making them an inevitable investment. But they are not easy processes to conduct, often requiring the help of an external professional agency, especially for startups and other SMBs.

If you’re a business owner who wants to adopt these technologies with the requisite annotation and labeling for them but is unsure about how to go about it, then continue reading to find the answer.

What are data annotation and labeling?

The technique of incorporating metadata into data is called data annotation. For machine learning algorithms to use a particular piece of data more intelligently, it is necessary to explain how and why it was gathered. Not only will data annotation improve your understanding of the data, but it will also make it simpler for anyone else who could use it.

Data labeling is similar to data annotation in that it involves assigning tags to things in raw data (such as photographs and videos) to assist machine learning (ML) models in recognizing them and making predictions and estimates. As an illustration, security AI can identify the object a person is carrying to determine whether or not they pose a threat.

Instances of data annotation aiding AI/ML model development

Data use cases for business are vast and varied, demanding that annotators use an equally varied set of annotation techniques to accomplish the respective goals. For instance, you have text annotation, image annotation, and audio-based annotation with various subtypes. Each has its strengths and weaknesses and gives models that work on and develop those characteristics.

So, you can expect the following outcomes when you apply the techniques:

Creation of self-learning ML models via Deep Learning

AI and ML models get better with more examples annotated and fed to them for training purposes. The lack of human-like learning abilities in computers means that they require supervised machine learning, which is humans providing annotated data to begin and carry forward their training. But, this is impractical when there are hundreds of thousands of pieces of data that need to be annotated manually.

This is where data annotation comes to the rescue by enabling developers to create ML models that can annotate and train themselves. Deep learning is one of the biggest factors driving smarter AI/ML model development. Here, instead of a single ML algorithm that is fed manually annotated data for training purposes, you have many algorithms structured hierarchically. Each successive algorithm takes the result of the previous one and gives out more accurately annotated data.

Thus, after an initial supervised learning phase, the algorithms proceed with unsupervised learning by annotating the data and learning about it independently.

Improvement of target subject/object recognition

The accuracy of an AI/ML model rests squarely on how well it can distinguish its intended subject/object in a given data set. For that, it needs to be able to recognize that entity from its background with a high degree of precision. Annotation and labeling are critical to giving an algorithm this ability.

For example, in image annotation, the classification technique lets you tag images/video frames and group them under various classes to help algorithms recognize and distinguish entire images/frames from others. That is followed by object detection and recognition that helps discover more information about the target subject/object like its location, size, etc., within images.

Another type of annotation that forms a crucial component of a model’s predictive accuracy is boundary identification. Here, the boundaries that demarcate target objects and subjects from the rest of the data themselves are the target. This type of annotation helps with deep learning mentioned earlier, where annotation and identification are automated. It is also how lines and curves are identified in image data.

With such data annotation, the final model will be able to identify the target subject/object whenever a new, unfamiliar data set is fed into the system. There are equivalent techniques for text and audio data as well, which can be combined to result in a model that can successfully extract required data irrespective of the data type available.

Elimination of data bias

Data bias is when an annotator introduces some form of bias unknowingly into the model due to it being the norm in their social circumstances. Such data bias can skew model development in that direction and lead to diminished accuracy. In some cases, it could lead to negative consequences for the people it is biased against. Thus, there is a need for ethics to form a core component of AI/ML development, and annotation is the answer.

The best way to eliminate such biases is to include as many examples as possible of a variety of datasets containing target subjects/objects. As established earlier, such large quantities of data can be analyzed for training using deep learning made possible by data annotation.

An example of this is speech recognition, where the system should recognize English words spoken with different accents. A model trained using predominantly North American accents will have issues distinguishing words when the same are spoken by a person from a different part of the world. Smart home speakers are a good case study here, as manufacturers of such systems have faced and continue to face issues related to wrong interpretation of user commands.

The problem is exacerbated when local languages are to be used since English is the de facto development language and translation needs to happen in both directions during processing. There is a strong possibility of errors being introduced into the system in such instances that can be minimized through accurate data labeling and annotation of audio data sets of both languages.

Techniques for getting the best data labeling and annotation results

Paying attention to the implementation of challenging procedures like data annotation and labeling is critical. Or else the results won’t be as expected. Some of the best strategies to stop it are as follows:

  • Collect a range of information. Images, for instance, should depict the same item from many angles and lighting setups. It aids in overcoming prejudice and prevents algorithm confusion.
  • Additionally, the data should be accurate, meaning that it should only be on the intended subject and nothing else that is similar to improve accuracy.
  • A robust Q and A method should be used to ensure that the annotation/labeling quality is high. Many methods can be applied, including task auditing, targeting, and random QA.
  • Follow a well-planned annotation guideline that carefully describes annotation and tool usage rules. When generating labels, give examples if necessary.
  • Build a seamless, efficient pipeline for your project to cut costs and delivery times.
  • Keep the lines of communication open with all involved parties through a range of activities and platforms.
  • Run a pilot to see how well your annotation setup functions. Analyze the outcomes and consider feedback to keep improving the process until you get the intended results.

In Conclusion

The ever-changing market conditions stress innovation, resiliency, adaptability, and efficiency being the foundations of business operations across industries to deliver consistent results. Technology in the form of AI and ML is critical to achieving these outcomes, and those, in turn, depend on accurate, quick Data Annotation. Along with Data Labeling, it can transform the models to deliver on the criteria mentioned to help you stay ahead of the competition and increase profitability.

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