Labeled data for ML: what, why, and how?

Overview

In this 20-minute webinar, we introduce data labeling for machine learning. We touch on how the process of working with data changes at different stages and explain why data is more important for ML than you may have thought. We also present some common data use cases and share the essential points to keep in mind when working with data.

• Why do we need to collect and label data?
• Can we use pre-made datasets?
• What influences the need for data and data labeling?
• Who collects and labels data?

Speaker

Kate Senicheva

TolokaKate is a Machine Learning Product Manager at Toloka AI. She has two years of experience building ML-based products, including ranking systems, quality instruments, NLG and voice assistants.