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Data Quality and Pre-Processing for Edge AI Success
Learn how to build high-quality datasets that deliver real-world results
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Data is the bedrock of any successful machine learning project — and bad data can’t be fixed with better models. In this webinar, Edge Impulse’s Teague Gudemann will offer a practical walk-through on building high-quality datasets that deliver real-world results. Whether you're just starting out or scaling to production, you'll learn actionable tips to improve your data pipeline, labeling workflow, and model performance.
We’ll cover:
- Why high-quality, representative data is more important than sheer volume
- How to start small and still see meaningful results using the 80/20 rule
- Ways to combine LLM-based automation with human oversight for efficient labeling
- Best practices for organizing, versioning, and maintaining datasets
- How to use feedback loops to improve real-world model accuracy over time
Our Presence:
Speakers:
Teague Gudemann
Teague Gudemann is a seasoned technology professional with over a decade of success in technology sales, who has passionately transitioned into the realm of machine learning. Currently at Edge Impulse, Teague has empowered hundreds of customers to transform their ML ideas into reality, expertly guiding them from initial concept to successful deployment with tailored tools, strategies, and deep industry expertise. His mission is to democratize machine learning, making it more accessible and impactful for businesses, and enabling them to unlock new possibilities in embedded AI. Teague holds a degree in International Business from the University of Oregon, which complements his global perspective and innovative approach in the tech landscape.
Speakers sessions:
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