Practical Approaches to Organizing Data Labeling for Machine Learning

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Data labeling is quite relevant, and it’s a serious business. There are several approaches you can go for when you want to label data. Your choice of data labeling approach depends on several factors, and one of them is the complexity of the problem that you want to solve.

You should also consider the size of the data science team and other resources, such as financial and time, that your company can allocate to implement the project.

Practical Approaches to Organizing Data Labeling for Machine Learning

Examples of Approaches used for Data Labeling

  • Crowdsourcing

When you choose to use crowdsourcing, you don’t have to recruit a data science team. The platforms offer an on-demand workforce. The image labeling software is well-designed, and with an easy-to-use interface for creating image labels. All you need is to do is to register as a requester, create, and manage your project.

Due to a significant workforce, you can have about a thousand images labeled in a few hours, instead of days or weeks.

  • In-house labeling

When you want to conduct a sentiment analysis of your company’s social media, you should consider using the internal approach of labeling. This allows you to evaluate the reputation and progress of your company and also helps you to research industry trends so that you can define the development strategy. You need to collect and label more than 90,000 reviews to build a model that performs adequately. This platform depends on teams of data labeling experts and is mostly used in projects such as space, finance, energy, or healthcare.

  • Synthetic labeling

This approach involves generating data by imitating the real data in terms of crucial parameters that you’ll set. The synthetic data is produced when you use a generative model to train and validate data on an original dataset. However, there are three types of generative models, namely Variational Autoencoders (VAEs), Autoregressive models (ARs), and Generative Adversarial Networks (GANs). A good example is that you can use a synthetic transactional dataset to evaluate the efficiency of existing systems and develop a better one.

  • Outsourcing to companies

Instead of relying on a crowd or hiring temporary employees, you can seek the assistance of a reputable outsourcing company that has specialized in training data preparation. You should consider contacting a company that analyzes text, images, and video. The image labeling software is very accurate and precise to give the desired results.

  • Data programming

If you’re looking forward to training data creation and management, the managing approaches and tools described above offers the best solution and eliminate the need for manual labeling. The image labeling software uses a data analysis engine and scripts for automation of labeling.

Conclusion

When looking for dataset labeling tools, you should know that they come in different packages, some are free, and others are paid for. In most cases, the free tools provide you with only the necessary annotation instruments and commercial tools, including additional features. You should choose the labeling tools that meet your business needs.

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