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From ML Engineering to AI Engineering: Navigating the Shift to Foundation Models

Date & Time

July 25, 2024, 7 p.m. - July 25, 2024, 9 p.m.

Cost

$0

Location

Online


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Description

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**RSVP instructions: register on event website to receive joining link. (RSVP on meetup will NOT have joining link)

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.

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