Amazon Mechanical Turk. The online market place for work. We give businesses and developers access to an on-demand scalable workforce. Workers can work at home and ...

Want to make money in your spare time? Become an Amazon Mechanical Turk (MTurk) Worker and start applying your skills to the thousands of available tasks in the MTurk marketplace.

Amazon Mechanical Turk (MTurk) provides a service for service requesters (hereafter “Requesters”) to integrate Artificial Artificial Intelligence directly into their applications by making requests of humans.

Every day Workers on Amazon Mechanical Turk (MTurk) help Requesters solve a range of data processing, analysis, and content moderation challenges. This is made possible by the contributions from Workers around the world that power the MTurk marketplace.

Get Started with Amazon Mechanical Turk. Create Tasks. Human intelligence through an API. Access a global, on-demand, 24/7 workforce. Create a Requester account. or. Make Money. Make money in your spare time. Get paid for completing simple tasks. Request a Worker account ...

Amazon Mechanical Turk (MTurk) is a crowdsourcing marketplace enabling individuals and businesses (known as Requesters) to engage a 24/7, global distributed workforce (known as Workers) to perform tasks.

One of the best ways to understand Amazon Mechanical Turk (MTurk) is to add a HIT to the MTurk marketplace and see what the experience is like. Get started with Amazon Mechanical Turk and try it out for yourself.

Amazon Mechanical Turk (MTurk) is a marketplace for completion of virtual tasks that requires human intelligence. The Mechanical Turk service gives businesses access to a diverse, on-demand, scalable workforce and gives Workers a selection of thousands of tasks to complete whenever it's convenient.

Simplify using Amazon Mechanical Turk in your applications with an API tailored to your programming language or platform.

Amazon Mechanical Turk makes it possible to generate human-annotated data for machine learning algorithms quickly and at scale. By harnessing the power of the crowd to obtain high-quality labeled data, we were able to measure and build effective models applicable across the media spectrum.”