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Through clearly defined microtasks, data scientists can quickly identify pedestrians and vehicles within images, decode text in handwritten notes, rate the quality of search results, or verify business addresses. This article outlines the many benefits of crowdsourced data labeling, tips for selecting a crowdsourcing partner, and best data labeling companies on the market.
Data science teams have the choice between labeling data in-house or outsourcing to a firm that specializes in crowdsourced services. Rather than hiring thousands of temporary employees, outsourcing your data labeling workload allows you to distribute thousands of tasks to a virtual workforce, taking the burden off of internal data engineers.
If you plan on labeling data in-house, you’ll need to invest in developing annotation tools from scratch or licensing them from a third party. Furthermore, you’ll have to onboard and train the annotators themselves. Generally speaking, you don’t want to handle the process in-house if you lack the bandwidth or engineering capabilities. Working with an experienced crowdsourcing partner can make all the difference in helping you achieve maximum return on investment.
Crowdsourcing companies vary in the features they offer, data security practices, storage options, and more. Here are a few critical factors to keep in mind when evaluating service providers:
Last but not least, the effective use of pilot projects is crucial to crowdsourcing success. One of the primary benefits of crowdsourcing platforms is the ability to quickly modify tasks by first testing them on small groups of crowdworkers. You should always request a pilot project before committing to a crowdsourcing partner.
Ultimately, the right crowdsourcing partner will depend on your project’s scope, scale, budget and timeline. To help you find the perfect partner, below we will introduce eight of the best data labeling companies for machine learning.
Crowdsourcing platforms like Amazon Mechanical Turk and Lionbridge AI assign data labeling tasks to a distributed workforce to perform online. The best crowdsourcing companies can help you achieve the quality of a trained in-house team at scale. Here are just a few of the best crowdsourcing companies for data science projects:
Data labeling is an indispensable stage of data preprocessing. Luckily for modern data scientists, crowdsourcing is an efficient option for outsourcing high volume data labeling tasks to an on-demand workforce.
If you’re looking for a quick and easy way to label data get in touch with Lionbridge AI. We make data labeling easy with our intuitive platform: simply upload data, add your team, and build custom datasets in hours. In addition to our data labeling platform, Lionbridge AI unlocks access to 1,000,000+ qualified annotators that can quickly and precisely label datasets.