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Recent breakthroughs in AI have not only increased demand for AI products, they've also lowered the barriers to entry for those who want to build AI products. The model-as-a-service approach has transformed AI from an esoteric discipline into a powerful development tool that anyone can use. Everyone, including those with minimal or no prior AI experience, can now leverage AI models to build applications. In this book, author Chip Huyen discusses AI engineering: the process of building applications with readily available foundation models. The book starts with an overview of AI engineering, explaining how it differs from traditional ML engineering and discussing the new AI stack. The more AI is used, the more opportunities there are for catastrophic failures, and therefore, the more important evaluation becomes. This book discusses different approaches to evaluating open-ended models, including the rapidly growing AI-as-a-judge approach. AI application developers will discover how to navigate the AI landscape, including models, datasets, evaluation benchmarks, and the seemingly infinite number of use cases and application patterns. You'll learn a framework for developing an AI application, starting with simple techniques and progressing toward more sophisticated methods, and discover how to efficiently deploy these applications. Understand what AI engineering is and how it differs from traditional machine learning engineering Learn the process for developing an AI application, the challenges at each step, and approaches to address them Explore various model adaptation techniques, including prompt engineering, RAG, fine-tuning, agents, and dataset engineering, and understand how and why they work Examine the bottlenecks for latency and cost when serving foundation models and learn how to overcome them Choose the right model, dataset, evaluation benchmarks, and metrics for your needs Chip Huyen works to accelerate data analytics on GPUs at Voltron Data. Previously, she was with Snorkel AI and NVIDIA, founded an AI infrastructure startup, and taught Machine Learning Systems Design at Stanford. She's the author of the book Designing Machine Learning Systems, an Amazon bestseller in AI. AI Engineering builds upon and is complementary to Designing Machine Learning Systems (O'Reilly).

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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.

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