Title
Machine Learning Engineering,Used
Sold by Ergodebooks, an authorized reseller.
Returns accepted within 30 days | support@ergodebooks.com
Shipping Information
- Free Standard Shipping — United States only
- Processing Time: 1–3 business days
- Estimated Delivery: 3–5 business days after dispatch
- Double-boxed, fully insured & discreetly packaged
- Tracking number sent via email once dispatched
- Orders over $250 require signature upon delivery. Taxes calculated at checkout.
Returns & Refund
Returns accepted within 30 days of delivery.
Damaged or Defective Item
Free return shipping + replacement or full refund
Wrong Item Received
Free return shipping + replacement or full refund
Change of Mind
Return shipping at customer's expense · 25% restocking fee applies
Payment Option
The most comprehensive book on the engineering aspects of building reliable AI systems."If you intend to use machine learning to solve business problems at scale, I'm delighted you got your hands on this book."Cassie Kozyrkov, Chief Decision Scientist at Google"Foundational work about the reality of building machine learning models in production."Karolis Urbonas, Head of Machine Learning and Science at Amazon
⚠️ WARNING (California Proposition 65):
This product may contain chemicals known to the State of California to cause cancer, birth defects, or other reproductive harm.
For more information, please visit www.P65Warnings.ca.gov.
- Q: What are the dimensions of the book? A: The book measures seven point five two inches in length, one inch in width, and nine point two five inches in height. It's designed to be a standard hardcover format.
- Q: How many pages does the book contain? A: The book has three hundred ten pages. This comprehensive content covers various aspects of machine learning engineering.
- Q: What is the binding type of this book? A: The book is bound in hardcover. This provides durability and a premium feel, making it suitable for frequent use.
- Q: How should I approach reading this book? A: You can read this book at your own pace, as it is suitable for both beginners and those with some prior knowledge of machine learning. The chapters are structured to build upon each other.
- Q: Is this book suitable for beginners in machine learning? A: Yes, the book is suitable for beginners. It provides foundational knowledge while also addressing more advanced topics for experienced readers.
- Q: Are there any prerequisites for understanding this book? A: While some background in programming and statistics is helpful, it is not mandatory. The book is designed to guide readers through complex concepts clearly.
- Q: How should I store this book to keep it in good condition? A: Store the book upright on a shelf to prevent warping. Keeping it in a dry, cool place will also help maintain its quality.
- Q: Can I clean the book if it gets dirty? A: Yes, you can wipe the cover gently with a damp cloth. Avoid using any harsh cleaning agents to protect the book's surface.
- Q: What should I do if the book arrives damaged? A: If the book arrives damaged, you should contact the seller for a return or exchange. Always check the return policy before purchasing.
- Q: Is there a warranty for this book? A: No, this book does not come with a warranty. However, you can return it if it is defective or damaged upon arrival.
- Q: How does this book compare to other machine learning resources? A: This book is unique because it focuses on the engineering aspects of machine learning. It provides a more practical approach compared to purely theoretical resources.
- Q: Is this book suitable for professionals in the field? A: Yes, professionals will find this book valuable. It offers insights into building reliable AI systems, which is crucial for anyone working in machine learning.
- Q: What genre does this book fall under? A: The book falls under the Business Technology genre. It is specifically tailored for those interested in machine learning engineering.
- Q: How does the author’s background affect the content? A: The author, Andriy Burkov, has extensive experience in machine learning. His insights and expertise lend credibility and depth to the content.
- Q: What can I expect to learn from this book? A: You can expect to learn about the practical aspects of building and deploying machine learning models. The book covers various methodologies and best practices.