The process of training and evaluating machine learning (ML) models relies on high-quality and timely annotated datasets. While a significant portion of academic and industrial research is focused on creating new ML methods, these communities rely on open datasets and benchmarks. However, practitioners often face issues with unlabeled and unavailable data specific to their domain. We believe that building scalable and sustainable processes for collecting data of high quality for ML is a complex skill that needs focused development. To fill the need for this competency, we created a semester course on Data Collection and Labeling for Machine Learning, integrated into a bachelor program that trains data analysts and ML engineers. The course design and delivery illustrate how to overcome the challenge of putting university students with a theoretical background in mathematics, computer science, and physics through a program that is substantially different from their educational habits. Our goal was to motivate students to focus on practicing and mastering a skill that was considered unnecessary to their work. We created a system of inverse ML competitions that showed the students how high-quality and relevant data affect their work with ML models, and their mindset changed completely in the end. Project-based learning with increasing complexity of conditions at each stage helped to raise the satisfaction index of students accustomed to difficult challenges. During the course, our invited industry practitioners drew on their first-hand experience with data, which helped us avoid overtheorizing and made the course highly applicable to the students’ future career paths.

In this tutorial, we present a portion of unique industry experience in efficient data labeling via crowdsourcing shared by both leading researchers and engineers from Yandex. We will make an introduction to data labeling via public crowdsourcing marketplaces and will present the key components of efficient label collection. This will be followed by a practice session, where participants will choose one of the real label collection tasks, experiment with selecting settings for the labeling process, and launch their label collection project on one of the largest crowdsourcing marketplaces. The projects will be run on real crowds within the tutorial session. While the crowd performers are annotating the project set up by the attendees, we will present the major theoretical results in efficient aggregation, incremental relabeling, and dynamic pricing. We will also discuss their strengths and weaknesses as well as applicability to real-world tasks, summarizing our five year-long research and industrial expertise in crowdsourcing. Finally, participants will receive a feedback about their projects and practical advice on how to make them more efficient. We invite beginners, advanced specialists, and researchers to learn how to collect high quality labeled data and do it efficiently.

In this tutorial, we present a portion of unique industry experience in efficient data labelling via crowdsourcing shared by both leading researchers and engineers from Yandex. We will make an introduction to data labelling via public crowdsourcing marketplaces and will present key components of efficient label collection. This will be followed by a practice session, where participants will choose one of the real label collection tasks, experiment with selecting settings for the labelling process, and launch their label collection project on Yandex.Toloka, one of the largest crowdsourcing marketplaces. The projects will be run on real crowds within the tutorial session. Finally, participants will receive a feedback about their projects and practical advice to make them more efficient. We expect that our tutorial will address an audience with a wide range of background and interests. We do not require specific prerequisite knowledge or skills. We invite beginners, advanced specialists, and researchers to learn how to efficiently collect labelled data.