Automating Data Annotation with LLMs // LLMs in Production Conference 3 Workshop1

// Abstract
This talk dives into using Large Language Models (LLMs) for data annotation automation. We'll cover techniques and workflows to run automation with LLMs, and discuss how to interpret, validate, and use these results in subsequent machine learning pipelines, such as model finetuning. Join to understand the practical side of LLM-driven data annotation.

// Bio
Nikolai Liubimov
PhD in Computer Science, AI expert with a history in industrial-strength machine learning, including large-scale Conversational AI development. Currently, as the CTO of @HumanSignal, spearheading technological evolution and charts strategic tech directions.
Research is anchored in Data-centric AI, programmatic labeling, weak supervision, zero/few-shot learning, and exploring Large Language and other Foundation model applications.

Michael Malyuk
Michael is an engineer turned entrepreneur with a great passion for AI systems. Currently, he works @HumanSignal. At HumanSignal, they aim to help companies transform into AI-powered enterprises by capturing and utilizing human signals to build, refine, and validate models. They strive to automate as many steps as possible, allowing human expertise to focus solely on tasks that add unique value to the models. They're creating a system that facilitates data discovery, knowledge encoding, model supervision, and business automation.

Chris Hoge
Chris Hoge is the Head of Community for @HumanSignal, helping to grow the Label Studio community. He has spent over a decade working in open source machine learning and infrastructure communities, including Apache TVM, Kubernetes, and OpenStack. He has an M.S. in Applied Mathematics from the University of Colorado, where he studied numerical methods for simulating physical systems. He makes his home in the Pacific Northwest, where he spends his free time trail running and playing piano.

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