Education, collaboration, and support for those working with data.
AI-based products and services rely on large amounts of high-quality labeled data for training, tuning, and evaluating machine learning algorithms. We strongly believe that machine learning workflows will focus increasingly on data production.
The crowdsourcing approach is a popular way to collect and label large datasets with faster turnaround and lower costs compared to using a limited group of experts for data collection and annotation. Our 10 years of industry experience and research show that building top-quality datasets requires a strict methodology.
We created this series of video tutorials to get you acquainted with crowdsourcing - through theory and real-life case studies. Our guide consists of 3 tracks: ML and AI, Business and Marketing, and Social Sciences. Whatever your background, you can count on our tutorials to walk you through the basics.Learn more
Our special offer for students, faculty members, and researchers gives you the opportunity to collect and label data with zero commission. Apply now and tell your colleagues and students about this pricing plan.
#students #faculty #researchers
We believe that it is essential to support those who educate. That’s why we have accumulated all we have to offer in one place. To see what we have for educators, go to Learn More.
We are passionate about connecting crowdsourcing enthusiasts with companies looking for crowd solutions architects. Check out open positions on the job board.
Toloka partners with universities across the world to incorporate crowd science techniques into coursework.
Improved crowdsourced translations of product descriptions.
budget reduction while achieving optimal quality.
Improved the accuracy
of a predictive tool using local shopping patterns.
improvement in app accuracy after data collection, reaching 95%.
search relevance tasks completed in one month for $1500.
Tuned a 3D foot sizing
app for better accuracy.
improvement in app accuracy with accelerated time to market.