Training data
for machine learning
Take full control of your data labeling process, timeframe and quality.
Build reliable algorithms with quality datasets of any size.
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Data labeling for ML models
With Toloka, you can control the accuracy of data labeling to develop high performing ML models.
Real-time insights
Track your projects with real-time statistics on progress, spending, quality, time spent on tasks and active users involved. Leverage detailed analytics to fine-tune as necessary and make timely decisions to optimize speed, quality and budget.
Success stories
Alexey Hahunov, founder and CTO of Dbrain, IT company and Y Combinator alumnus:
“Toloka helped us tackle the challenge of recognizing handwritten text in documents. We send anonymous text fragments to Toloka, where qualified performers rewrite the text and send the results back to the Dbrain system for further AI training. This meant we were able to resolve even the most difficult recognition cases for our customers.”
Konstantin Simonchik, science director and co-founder of ID R&D:
“Using Toloka, we collected the world’s largest database of 200,000 unique photos and videos to secure biometric systems against hackers and train neural networks to distinguish real faces from fakes. We chose Toloka because of the user-friendly interface, active performers and optimal quality for the price.”
Dmitry Akimov, VisionLabs data engineer:
“Toloka is an excellent tool for data processing. We use it to collect and annotate thousands of images and videos every month in order to improve the performance of our algorithms.
We chose Toloka because of the fast turnaround time and the active participation of performers.”
Mikhail Burtsev, head of the DeepPavlov.ai laboratory for neural networks and deep learning at Moscow Institute of Physics and Technology:
“Modern machine learning technologies are not possible without a large volume of training examples. A computer can’t master a natural language unless a person ‘explains’ how to use it correctly. Toloka allows the conversational AI systems that we develop at MIPT to communicate with a large group of people and grow smarter.”
Angelina Galkevich, Analyst Tinkoff:
“We use Toloka to annotate data for speech synthesis and recognition Toloka performers have already processed tens of thousands of tasks with audio files for us, and we have successfully used the results to train our models. We like the price, the fast turnaround, and the super fast feedback. It allows us to quickly test different markup methods and hypotheses, and then choose the optimal approach.”
Anton Slesarev, head of Technologies in the self-driving cars division at Yandex:
“Toloka is the first place we go to prepare data for AI. To train the neural network, we need tens of thousands of annotated images. You can buy them for $4 each, or you can have images marked up in Toloka 10 times cheaper and get datasets that are ready to use for training algorithms. We use these datasets for developing self-driving vehicle technology.”
Yandex.Maps Team:
“Our service tracks outdated information on Yandex.Maps. We update business hours, add new businesses, and remove any that have closed. Toloka users perform our tasks in their neighborhoods, like taking photos of business signs with operating hours.
Every month we distribute 15 million tasks like this to 50,000 performers — this is the only way we can keep the huge Yandex.Maps database up to date.”
Alexey Hahunov, founder and CTO of Dbrain, IT company and Y Combinator alumnus:
“Toloka helped us tackle the challenge of recognizing handwritten text in documents. We send anonymous text fragments to Toloka, where qualified performers rewrite the text and send the results back to the Dbrain system for further AI training. This meant we were able to resolve even the most difficult recognition cases for our customers.”
Konstantin Simonchik, science director and co-founder of ID R&D:
“Using Toloka, we collected the world’s largest database of 200,000 unique photos and videos to secure biometric systems against hackers and train neural networks to distinguish real faces from fakes. We chose Toloka because of the user-friendly interface, active performers and optimal quality for the price.”
Dmitry Akimov, VisionLabs data engineer:
“Toloka is an excellent tool for data processing. We use it to collect and annotate thousands of images and videos every month in order to improve the performance of our algorithms.
We chose Toloka because of the fast turnaround time and the active participation of performers.”
Mikhail Burtsev, head of the DeepPavlov.ai laboratory for neural networks and deep learning at Moscow Institute of Physics and Technology:
“Modern machine learning technologies are not possible without a large volume of training examples. A computer can’t master a natural language unless a person ‘explains’ how to use it correctly. Toloka allows the conversational AI systems that we develop at MIPT to communicate with a large group of people and grow smarter.”
Angelina Galkevich, Analyst Tinkoff:
“We use Toloka to annotate data for speech synthesis and recognition Toloka performers have already processed tens of thousands of tasks with audio files for us, and we have successfully used the results to train our models. We like the price, the fast turnaround, and the super fast feedback. It allows us to quickly test different markup methods and hypotheses, and then choose the optimal approach.”
Anton Slesarev, head of Technologies in the self-driving cars division at Yandex:
“Toloka is the first place we go to prepare data for AI. To train the neural network, we need tens of thousands of annotated images. You can buy them for $4 each, or you can have images marked up in Toloka 10 times cheaper and get datasets that are ready to use for training algorithms. We use these datasets for developing self-driving vehicle technology.”
Yandex.Maps Team:
“Our service tracks outdated information on Yandex.Maps. We update business hours, add new businesses, and remove any that have closed. Toloka users perform our tasks in their neighborhoods, like taking photos of business signs with operating hours.
Every month we distribute 15 million tasks like this to 50,000 performers — this is the only way we can keep the huge Yandex.Maps database up to date.”