Data Labeling Approaches for Machine Learning

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Data Labeling is one of the key factors that determine the quality of a machine learning project. Although data labelling tasks are time-consuming and can get very complex, by selecting the right approach, your machine learning project can steer clear of any quality or accuracy hurdles.

In this blog, we have listed out 5 data labeling approaches for Machine Learning projects along with their pros and cons.

Data Labeling for Machine Learning

Internal Labeling

As the name suggests, the data labeling tasks are performed by an in-house team. Internal labeling can help you achieve the highest level of accuracy and also allows you to track the progress. This means your ML models will predict good results and you will have complete control over the data labeling process. But, it is a very slow process when compared to other data labeling approaches. Hence, you should opt for this approach if your company has enough time, human and financial resources.

Outsourcing

You can create a team of freelancers who provide data labeling services to speed up your ML development. You can find them on recruitment and social networking sites. You can also easily find them on freelancing sites like UpWork. This approach allows you to get the right people onboard since you check for the freelancer’s skills with tests.

Outsourcing mostly entails small to mid-sized teams. Hence you will be able to control their work. But the drawback of this approach is that you will have to build an intuitive workflow and that requires some amount of planning. You should also be able to provide them with the right tools to finish their job.

Crowdsourcing

Crowdsourcing platforms give you access to datalabelers from across the world. It is one of the cost-effective approaches and you can get the data labeled in a quick time. The quality of the workers and quality assurance may vary from platform to platform. Hence when choosing a crowdsourcing platform, it is best to check for workers’ quality, QA, and the tools they use to manage data labelers and projects.

Data Programming

This approach involves the method of using scripts to label data automatically. The programming approach not only gets your data labeling done quickly but also reduces the need for human data labelers. It is often combined with a QA team as the processes are still far from being perfect.

Synthetic Labeling

Synthetic labeling involves the generation of data having the required parameters set by the user for real data. Generative models that are trained and validated using an original dataset are used to produce synthetic data. There are three types of generative models – Variational Autoencoders, Generative Adversarial Networks, and Autoregressive models. This approach to data labeling is fast and cheaper but may require high computational power to render and train the model further.

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Data Labeling Approaches for Machine Learning

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