Details
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Description:
Chip Huyen is a writer and computer scientist currently at Voltron Data, working on GPU-native data processing and open data standards (Ibis, Apache Arrow, Substrait). Previously, she built machine learning tools at NVIDIA, Snorkel AI, and Netflix.
In this fireside chat, Chip joins Hugo Bowne-Anderson to explore the unique challenges and opportunities in productionizing foundation models compared to traditional machine learning approaches. As AI systems become increasingly advanced and open-ended, ML engineers must adapt their strategies and techniques to ensure reliable, efficient, and scalable deployments. Key topics of discussion will include:
- From closed-ended to open-ended evaluation: Developing robust evaluation methodologies for foundation models, which can generate novel outputs and exhibit emergent behaviors;
- From feature engineering to context construction: Techniques for effectively prompting and guiding foundation models to perform desired tasks across diverse domains;
- Adapting to unstructured data: Strategies for processing and integrating the vast amounts of unstructured data required to train and operate foundation models;
- Infrastructure and tooling challenges: Scaling compute resources, optimizing workflows, and building reliable pipelines for foundation model deployment;
- The evolving role of the ML engineer: New skills, collaborations, and best practices for succeeding in the era of foundation models and AI-driven products.
Join us for a conversation on the forefront of AI engineering, and discover strategies for navigating the shift to foundation models in your organization.
Sponsors:
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