LinkedAI

In this blog post, we hope to give you a simplified guide to data labeling that offers the basics of what you need to know so you get high quality labels for your organizational data.

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Data labeling is the process of detecting and adding tags to raw data samples — images, text, audio files, videos, and others — so that ML algorithms can then learn from them. Informative labels in machine learning can provide more context and meaning to the data, allowing ML models to improve accuracy of predictions and estimations. The entire data labeling workflow generally includes tasks such as data tagging, annotation, classification, moderation, transcription, and processing.

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Now just because a piece of data is classified as unlabeled doesn’t mean that it’s rendered unusable. Both labeled and unlabeled data can be utilized for machine learning models, albeit in varying levels of usability.

Unlabeled Data is data that has not been tagged with labels that identify characteristics, properties, or classifications. Obtained using observation and collection, unlabeled data is used in unsupervised machine learning where, without the informative tags on them, the ML program would have to identify the data based on its natural properties and characteristics.
Labeled Data, on the other hand, is data that has been marked up or tagged so that they already identify the target or the answer which the machine learning model is trained to predict. This is used in supervised machine learning where the program performs analysis and produces actionable insights based on the labeled data that it has looked at and is training with. Labeled data in general, has to have been annotated by human experts, hence, it is more costly and difficult to acquire and store.

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A very simple example would be if a machine learning algorithm is being developed to differentiate three common animals, say a cat, dog, and rat. Labeled datasets that have properly tagged images of these three animals would allow the program to identify and classify them immediately. When unlabeled images are fed to the program however, the algorithm would have to classify them according to their properties, e.g. color, body shape, characteristics of ears, eye features — you get the picture.

Based on the above illustration, you can see how essential having labeled data is for building a high-performance ML model that delivers accurate results.

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Considering how crucial a quality label in machine learning is to developing an effective algorithm, organizations have to carefully consider the right path to efficient data labeling. Here are five common data labeling approaches:

In-house Labeling. This option entails using in-house data scientists and engineers to spearhead the process. While inhouse data labeling often ensures the highest quality tags, quality takes time and thus, the process is significantly lengthy.
Synthetic Labeling. This approach involves using synthetic data — data that’s been artificially generated by computer programs rather than real-life events. Synthetic data may be used partially to augment training data, saving money on the data collection process. Synthetic data does require extensive computing and storage resources, but with the rise of cloud options, synthetic labeling has become more accessible today.

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Crowdsourcing. The crowdsourcing approach makes use of the collective annotating efforts of numerous freelancers associated with a crowdsourcing platform. Crowdsourcing annotation is faster and less costly, although quality assurance and project management may differ across different crowdsourced data labeling platforms.
Programmatic. Programmed labeling is the automated process of using scripts to label data. This option reduces annotation time and eliminates the need for large numbers of human labelers.
Outsourcing. Using third-party data labeling services can be a sound option for high-level temporary projects that run over a set period of time. Outsourcing to data labeling platforms ensures that the project is in the hands of expert staff utilizing appropriate, pre-built data labeling tools. Quality assurance and data security are top considerations when acquiring data labeling services.

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Building successful ML models can only be done effectively when they are fed with massive amounts of high-quality labeled data. Whether your enterprise annotates with inhouse experts, uses programs and scripts, or crowdsources/outsources to data labeling platforms, it’s important to understand that machine learning and other AI algorithms can only be as good as the data they are trained with.

Learning the Basics: A Quick Guide to Data Labeling