Human-in-the-Loop Concept for Building Fully Adaptive ML Models Using Crowdsourcing

Human-in-the-Loop Concept for Building Fully Adaptive ML Models Using Crowdsourcing

Explore the concept of combating drift in machine learning through crowdsourcing in this 29-minute conference talk by Fedor Zhdanov, Head of AI at Toloka. Learn how to construct complex drift-monitoring systems and human-in-the-loop ML models that can be fully automated. Discover the concept of "adaptive ML models," including their construction and maintenance. Gain insights from Zhdanov's extensive experience in connecting ML and humans in human-in-the-loop processes, focusing on building responsible state-of-the-art AI-first business solutions with human oversight. Understand the importance of addressing concept drift in ML models and how crowdsourcing can be leveraged to create more robust and adaptable systems.