Ingénierie de l'IA - Concevoir des applications avec des modèles de fondation

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).

Von der Idee zur KI-Anwendung - praxisnah und fundiert- Ein Leitfaden für alle wesentlichen Aspekte der Entwicklung generativer KI-Systeme, inklusive Prompt Engineering, RAG, Finetuning und Agenten- Das Buch bietet ein Framework für den Einsatz von KI in realen Anwendungen- Es deckt den kompletten Entwicklungszyklus von KI-Systemen ab, von der Modellentwicklung bis zum produktiven BetriebIn diesem Praxishandbuch beschreibt Bestsellerautorin Chip Huyen die Werkzeuge des KI-Engineerings und den Prozess der Erstellung von KI-Anwendungen mithilfe generativer Foundation Models. Das Buch deckt den kompletten Entwicklungszyklus von KI-Systemen ab, von der Modellentwicklung bis zum produktiven Betrieb.Entwickler und Entwicklerinnen von KI-Anwendungen erfahren, wie sie sich in der KI-Landschaft zurechtfinden - bei Modellen, Datensätzen, Bewertungs-Benchmarks und der scheinbar unendlichen Zahl von möglichen Anwendungsmustern. Das Buch bietet damit einen praxisorientierten Rahmen für die Entwicklung produktionsreifer generativer KI-Systeme - mit klarem Fokus auf aktuellen Tools, Herausforderungen und Potenzialen im KI-Engineering.- Erfahren Sie, was KI-Engineering ist und wie es sich vom traditionellen Machine-Learning-Engineering unterscheidet- Machen Sie sich mit dem Prozess der KI-Anwendungsentwicklung vertraut und lernen Sie die typischen Herausforderungen sowie mögliche Lösungsansätze kennen- Erkunden Sie verschiedene Techniken zur Modellanpassung wie Prompt Engineering, RAG, Finetuning, Agenten und Dataset Engineering und verstehen Sie deren Funktionsweise- Untersuchen Sie die Engpässe hinsichtlich Latenz und Kosten bei der Bereitstellung von Foundation Models und lernen Sie, wie Sie diese überwinden können- Wählen Sie das passende Modell und die richtigen Metriken, Daten und Entwicklungsmuster für Ihre Anforderungen aus

Machine learning systems are both complex and unique. Complex because they consist of many different components and involve many different stakeholders. Unique because they're data dependent, with data varying wildly from one use case to the next. In this book, you'll learn a holistic approach to designing ML systems that are reliable, scalable, maintainable, and adaptive to changing environments and business requirements. Author Chip Huyen, co-founder of Claypot AI, considers each design decision--such as how to process and create training data, which features to use, how often to retrain models, and what to monitor--in the context of how it can help your system as a whole achieve its objectives. The iterative framework in this book uses actual case studies backed by ample references. This book will help you tackle scenarios such as: - Engineering data and choosing the right metrics to solve a business problem - Automating the process for continually developing, evaluating, deploying, and updating models - Developing a monitoring system to quickly detect and address issues your models might encounter in production - Architecting an ML platform that serves across use cases - Developing responsible ML systems