{"slug": "mechanical-turk-shutting-down-september-30", "title": "Mechanical Turk shutting down September 30", "summary": "Amazon Mechanical Turk (MTurk), Amazon's crowdsourcing marketplace for microtasks and machine learning data annotation, will shut down on September 30, according to an announcement on its website. The platform, which has been used by companies and researchers to outsource tasks like data validation, content moderation, and ML training data creation, will cease operations, impacting its global on-demand workforce and clients who rely on it for human-in-the-loop workflows.", "body_md": "# Amazon Mechanical Turk\n\n## Access a global, on-demand, 24x7 workforce\n\nAmazon Mechanical Turk (MTurk) is a crowdsourcing marketplace that makes it easier for individuals and businesses to outsource their processes and jobs to a distributed workforce who can perform these tasks virtually. This could include anything from conducting simple data validation and research to more subjective tasks like survey participation, content moderation, and more. MTurk enables companies to harness the collective intelligence, skills, and insights from a global workforce to streamline business processes, augment data collection and analysis, and accelerate machine learning development.\n\nWhile technology continues to improve, there are still many things that human beings can do much more effectively than computers, such as moderating content, performing data deduplication, or research. Traditionally, tasks like this have been accomplished by hiring a large temporary workforce, which is time consuming, expensive and difficult to scale, or have gone undone. Crowdsourcing is a good way to break down a manual, time-consuming project into smaller, more manageable tasks to be completed by distributed workers over the Internet (also known as ‘microtasks’).\n\n# Benefits\n\n### Optimize efficiency\n\nMTurk is well-suited to take on simple and repetitive\ntasks in your workflows which need to be handled\nmanually. Using MTurk to outsource microtasks ensures\nthat work gets done quickly, while freeing up time and\nresources for the company – so internal staff can focus\non higher value activities.\n\n### Increase flexibility\n\nScaling up and down a workforce isn’t the easiest\nundertaking. With access to a global, on-demand, 24x7\nworkforce, MTurk enables businesses and organizations to\nget work done easily and quickly when they need it –\nwithout the difficulty associated with dynamically\nscaling your in-house workforce.\n\n### Reduce cost\n\nMTurk offers a way to effectively manage labor and\noverhead costs associated with hiring and managing a\ntemporary workforce. By leveraging the skills of\ndistributed Workers on a pay-per-task model, you can\nsignificantly lower costs while achieving results that\nmight not have been possible with just a dedicated\nteam.\n\n# How it works\n\nMTurk offers developers access to a diverse, on-demand workforce through a flexible user interface or direct integration with a simple API. Organizations can harness the power of crowdsourcing via MTurk for a range of use cases, such as microwork, human insights, and machine learning development.\n\n# Use Cases\n\n## Building, managing, and evaluating Machine Learning workflows\n\nMTurk can be a great way to minimize the costs and time\nrequired for each stage of ML development. It is easy to\ncollect and annotate the massive amounts of data required\nfor training machine learning (ML) models with MTurk.\nBuilding an efficient machine learning model also requires\ncontinuous iterations and corrections. Another usage of\nMTurk for ML development is human-in-the-loop (HITL),\nwhere human feedback is used to help validate and retrain\nyour model. An example is drawing bounding boxes to build\nhigh-quality datasets for computer vision models, where\nthe task might be too ambiguous for a purely mechanical\nsolution and too vast for even a large team of human\nexperts.\n\n“At AI2, we're pushing the state of the art of Artificial Intelligence, which often requires human-annotated data to train new systems and measure our progress. In particular, we use crowdsourcing platforms such as Amazon Mechanical Turk to build datasets that help our models learn common sense knowledge, which is often necessary to answer basic questions that are easy for humans but still quite hard for machines. Amazon Mechanical Turk provides a flexible platform that enables us to harness human knowledge to advance machine learning research.”\n\n– Michael Schmitz, Director of Engineering, Allen Institute for AI\n\n## Business process outsourcing\n\nA large, seemingly overwhelming task can sometimes be\ntransformed into a set of smaller, more manageable\nmicrotasks that can each be accomplished independently.\nCrowdsourcing can be an efficient organizational strategy\nto harness innovation and agility by distributing work to\nInternet users. Businesses or developers can use MTurk to\naccess thousands of on-demand workers—and then integrate\nthe results of that work directly into their business\nprocesses and systems. Common examples include the\nmoderation of web and social media content, categorization\nof products or images, and the collection of data from\nwebsites or other resources.\n\n“The F&B industry has always operated at the mercy of changing tastes and preferences of consumers. Our goal is to surface consumer insights and spot emerging trends, so our clients can effectively respond with effective strategies. Workers on Amazon Mechanical Turk respond to our requests to gather information from menus, websites, and other channels. We are able to leverage these human collective insights to better understand customer needs and uncover important market trends.”\n\n– David Falck, Executive Director, Food Genius / US\nFoods Data Science\n\nLearn more about Amazon Mechanical Turk\n\n[Visit the features page](/product-details)\n\n[Contact us](https://support.aws.amazon.com/#/contacts/aws-mechanical-turk)", "url": "https://wpnews.pro/news/mechanical-turk-shutting-down-september-30", "canonical_source": "https://www.mturk.com/", "published_at": "2026-08-26 23:55:44+00:00", "updated_at": "2026-08-27 00:19:26.474285+00:00", "lang": "en", "topics": ["artificial-intelligence", "machine-learning", "ai-products"], "entities": ["Amazon Mechanical Turk", "Amazon", "Allen Institute for AI", "Michael Schmitz"], "alternates": {"html": "https://wpnews.pro/news/mechanical-turk-shutting-down-september-30", "markdown": "https://wpnews.pro/news/mechanical-turk-shutting-down-september-30.md", "text": "https://wpnews.pro/news/mechanical-turk-shutting-down-september-30.txt", "jsonld": "https://wpnews.pro/news/mechanical-turk-shutting-down-september-30.jsonld"}}