Crowdsourcing Data Labeling for Machine Learning Projects [A How-To Guide]
Research suggests that data scientists spend 80% of their time preprocessing data and only 20% on building machine learning models. Crowdsourcing helps break down large and complex machine learning problems into smaller and simpler tasks for a large distributed workforce. This article outlines the many benefits of crowdsourced data labeling, tips for selecting a crowdsourcing partner, and best data labeling companies on the market. To help you find the perfect partner, below we will introduce the best data-labeling companies like Lionbridge AI and Amazon Mechanical Turk.
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Research suggests that data scientists spend a whopping 80% of their time preprocessing data and only 20% on actually building machine learning models. With that in mind, it’s no wonder why the machine learning community was quick to embrace crowdsourcing for data labeling. Crowdsourcing helps break down large and complex machine learning problems into smaller and simpler tasks for a large distributed workforce.
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.
In-house vs. Outsourced Data Labeling
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.
How to Select a Crowdsourced Data Labeling Partner
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:
- Experience: Does the vendor have an established track record of successful projects? Client logos, testimonials, and case studies allow you to get a closer look into the client’s background, solutions, and results. This also gives you an idea whether or not they’ve dealt with similar data types or file formats.
- Technology: A key benefit of working with an outsourced provider is access to pre-built tools. Be sure to ask what data labeling tools the company has built and what tools they use to manage their crowd and the quality of output.
- Quality: How does the company source and qualify workers on their platform? What kinds of quality assurance processes do they have in place?
- Security: Confidentiality is a major concern when outsourcing data labeling to a third-party. Be sure to ask about the security measures the vendor has in place to protect your data, as well as any certifications they may have, such as the widely used ISO certifications.
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.
The Best Data Crowdsourcing Companies
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:
- Lionbridge AI: Lionbridge’s data labeling platform makes it easy to collect data samples from thousands of qualified labelers in 300+ languages. With over 20 years of experience, Lionbridge has optimized the labeling process and built a data labeling platform to maximize efficiency and data quality.
- Amazon Mechanical Turk: Also known as MTurk, Amazon Mechanical Turk is a popular crowdsourcing marketplace commonly used for data collection. On Amazon Mechanical Turk, you can design, publish, and coordinate a wide range of human intelligence tasks (known as HITs), such as text classification, transcriptions, or surveys.
- Upwork: An online freelancer marketplace focused not on microtasks, but rather on larger scale jobs such as writing an article or designing a website.
- Scale: With a focus on computer vision applications, Scale offers a suite of managed labeling services via its annotation API to create the ground truth for machine learning models.
- ClickWorker: A German crowdsourcing platform that attracts European workers. It provides support for specialized tasks such as translation, web research, and web content generation. It also provides tools for mobile crowdsourcing.
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.
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