AI Engineering: Building Applications with Foundation Models.Pdf 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 developed the AI products. The model-as-a-service approach has transformed AI from an intended discipline for a powerful development tool that anyone can use. Everyone, including those with minimal or no prior AI experience, can now used the maximum advantage of AI models to build applications. In this book, Author Chip Huyen gives the detailed explanation about AI engineering ,then the process of building applications with readily available foundation models.
The book starts with a general overview of AI engineering, explaining how it differs from traditional ML engineering and discussing the new AI modules with the large number of scales. The maximum AI is used, the maximum opportunities there are for suffering failures, and therefore, the most important judgement becomes. This book discusses different level of approaches to evaluating boundary models, including the rapidly growing AI-total judgement approach.
AI application developers during a research how to navigate the automate the process of creating landscape, in which they includes models, datasets, evaluation benchmarks, and the apparently gives the infinite number of used cases and the application of various patterns. You’ll learn a essential supporting system of structure of a building a new developing an AI application, begins with the simple techniques and in the process of the particular destination of more simple but effective methods, and discover how to efficiently deploy these applications.
- Understand what is AI engineering and how it different from old machine learning to a new machine learning.AI engineering is the way to progress our life into the next level . Nowadays, from children’s to adults everyone wants to learn AI just because they don’t want feel like illustrate person.
- Learning the process of developing an AI application, is the challenges at each and every step, and approaches to address them.
- Explore various model process of adapting new techniques, including prompt engineering, RAG, fine-tuning, agents, and dataset engineering, and understand how to used ,when to used and need to deploy it and why they work into this special field.
- Examine the where the data flow is restricted and the cost when serving foundation of new models and learn how to overcome them.
- Choose the right model, dataset, evaluation benchmarks, and metrics for your needs.
Chip Huyen the author wants to works to accelerate data analytics on GPUs at Voltron Data. Before she was with Snorkel AI and NVIDIA, founded an AI infrastructure startup, and taught Machine Learning Systems Design at Stanford university. She’s the author of the book Designing Machine Learning Systems, which was an Amazon bestseller in AI.
AI Engineering builds upon and is complementary to Designing Machine Learning Systems
