 |
VOOZH | about |
|
VOOZH | about |
Download the free Kindle app and start reading Kindle books instantly on your smartphone, tablet, or computer - no Kindle device required.
Read instantly on your browser with Kindle for Web.
Using your mobile phone camera - scan the code below and download the Kindle app.
OK
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.
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).
Sharing the knowledge of experts
O'Reilly's mission is to change the world by sharing the knowledge of innovators. For over 40 years, we've inspired companies and individuals to do new things (and do them better) by providing the skills and understanding that are necessary for success.
Our customers are hungry to build the innovations that propel the world forward. And we help them do just that.
This book is for anyone who wants to leverage foundation models to solve real-world problems. This is a technical book, so the language of this book is geared toward technical roles, including AI engineers, ML engineers, data scientists, engineering managers, and technical product managers. This book is for you if you can relate to one of the following scenarios:
You can also benefit from the book if you belong to one of the following groups:
I love getting to the bottom of things, so some sections dive a bit deeper into the technical side. While many early readers like the detail, it might not be for everyone. Iโll give you a heads-up before things get too technical. Feel free to skip ahead if it feels a little too in the weeds!
Iโm Chip Huyen, a writer and computer scientist. I grew up chasing grasshoppers in a small rice-farming village in Vietnam.
I work in the intersection of AI, data, and storytelling. Previously, I built machine learning tools at NVIDIA, Snorkel AI, Netflix, and founded an AI infrastructure startup (acquired).
I also taught Machine Learning Systems Design at Stanford.
My last book, Designing Machine Learning Systems, is an Amazon bestseller in AI and has been translated into over 10 languages (very proud!).
In my free time, I like writing stories. I'm also the author of 4 Vietnamese story books.
Customer Reviews, including Product Star Ratings help customers to learn more about the product and decide whether it is the right product for them.
To calculate the overall star rating and percentage breakdown by star, we donโt use a simple average. Instead, our system considers things like how recent a review is and if the reviewer bought the item on Amazon. It also analyzed reviews to verify trustworthiness.
Learn more how customers reviews work on AmazonReading this book was fun and helped me connect dots of concepts that hitherto felt like just buzz words. After reading this book, I was prepped enough to read on higher level books. Highly recommended if you're just starting to learn AI and LLMs.
Love this book. Very insightful and extremely helpful in breaking down many of the complex aspects of AI so they are easy to understand!!!
It's always daunting to pick up a technical book that's over 500 pages long or 21 hours long. However, this book did not disappoint. Not every section, of course, addressed my particular needs. However, the entire treatise was clearly communicated with a broader technical audience in mind. That should be no surprise because Chip Huyen, besides being an AI expert, taught graduate school classes in AI at Stanford and writes science fiction as a side hobby. This book is simply the best technical introduction I've encountered to date.
The book starts with high-level concepts about AI, which would be accessible to all sorts of scientific folks. Then it focuses on technical topics that are of most interest to engineers. It does an excellent job of centering around concepts first and not being wedded to particular technologies which will soon change. I valued the insights so much that, after listening to the audiobook, I even bought a paper copy to have for a reference.
I plan to continue to read about AI engineering, but given that I haven't taken formal coursework in the topic, this book served as an equivalent to a graduate school class to give me confidence to dive deeper. Although some math were presented, the audiobook was incredibly accessible, unlike with some technical books. For those who spend time commuting in cars, I recommend listening to the text if you don't have time to flip through a paper book.
Overall, this book raised my game significantly about AI. Where other books obscure with technical jargon, this book enlightens with clear concepts. I still need to brush up on a few focused topics to ready myself for a project, but I'm much more fluent about the ideas than before. I highly recommend this in-depth introduction, at least for the next few years until the field outpaces our knowledge once again.
This should be the AI 101 for experienced developers who want to move into AI Engineering as a career option.
Good Job
Great resource.
Comprehensive survey of AI field today. Good introduction to different aspects of AI, instead of focusing only on the models.
Excellent content and quality print.
A bit pricey to what I usually buy, but I can confidently say "You get what you pay for"! I am so jealous of the author's clarity and easy tone that somehow manages to convey an impressive amount of information. In technical writing, if it looks easy, it certainly wasn't!
If I survive the technopocalypse, I look forward to reading more of Chip's books!
ูุชุงุจ ุฑุงุฆุน ุชุบููู ุฌูุฏ ูุงููุฑู ูุงููุชุงุจู ูุงุถุญู ุณุฑุนู ุจุดุญู ูุชูุตูู ุงูู ุนููู ุงุช ููู ููู ู ุฌุฏุง ุฌุฏุง ุฏุฎูุช ุฏูุฑุงุช ูุซูุฑ ู ุงุฃุณุชูุฏุช ุฒู ูุฐุง ุงููุชุงุจ ุฃูุตุญ ููู ูุจุดุฏู ู ู ู ุชุน ุฌุฏุง ูุณูู ุงูููู
I still need to learn more technical things to be able to understand all knowledge that this books brings but I learned a lot and will use as guide on this process. I strong recommend to anyone that want to start and donโt know where start
The central idea of the book is that foundation models have become so powerful and expensive to build that, instead of training models, many organizations might be better off creating applications on top of them. The book covers evaluation, guardrails, security, finetuning, context construction, inference optimization, user feedback and architecture.
The level of detail is excellent: we're looking under the hood just enough to understand what's going on, but keep that high level perspective that allows the book to give a overview of a broad topic in just 500 pages.
I highly recommended this book to engineers looking for an overview of AI engineering โ as opposed to ML engineering, which might be too low-level for them and be more relevant for data scientists.
Fantastic book and great and timely delivery
The book had exactly the level of depth I needed. Iโm coming from the data engineering side and needed some complete overview of AI Engineering. The book gave a complete coverage of the key topics while still going with some details (but avoiding the non-necessary technicalities). The reference are really valuable and worth the de-tour while reading.
