Hallo! Tracked shipping to Netherlands with Delivery Duty Paid for just €7 

Ship to
Netherlands
0

My Account

  • argentina
  • chile
  • colombia
  • españa
  • méxico
  • perú
  • estados unidos
  • internacional

menu

Select your country

Americas

Europe

Rest of the world

portada Data Engineering for AI and ML Pipelines: A Practical Guide to Building Reliable Data Systems for Machine Learning and Artificial Intelligence
Type
Physical Book
Publisher
Author
Language
English
Pages
296
Format
Paperback
ISBN13
9798160723440

Data Engineering for AI and ML Pipelines: A Practical Guide to Building Reliable Data Systems for Machine Learning and Artificial Intelligence

Sam, Ben A (Author) · Independently published · Paperback

Data Engineering for AI and ML Pipelines: A Practical Guide to Building Reliable Data Systems for Machine Learning and Artificial Intelligence - Sam, Ben A

New Book Imported to Netherlands
Delivery: 12 Nov - 19 Nov Shipping: 15 to 19 business days.
€ 51,08
Import costs and 9% BTW included in the price ✅
€ 51,08

Choose the list to add your product or create one New List

Go to My Wishlists
Calculate shipping cost

Synopsis "Data Engineering for AI and ML Pipelines: A Practical Guide to Building Reliable Data Systems for Machine Learning and Artificial Intelligence"

Your model is only as dependable as the data system that feeds it. Most machine-learning failures in production start in the data, not the model. A file arrives late, a schema changes without warning, or a feature is calculated one way in training and another in production. Data leakage can make a model look excellent until it goes live. Data Engineering for AI and ML Pipelines teaches you to build the systems that prevent these failures. The book is organized around six layers: Ingest, Transform, Validate, Store, Serve, and Monitor. One project runs through all of them. In 14 stages, you'll build a customer-churn pipeline for Northwind Stream, a fictional subscription business, taking it from raw files to a monitored, governed production system. Inside, you will learn how to: Ingest data reliably and enforce schemas and data contracts Transform, validate, and test data before it reaches a model Choose between warehouses, data lakes, and lakehouses, and model data for ML Engineer features and prevent leakage and training-serving skew Build pipelines that can be safely rerun and backfilled Serve features for batch and real-time predictions Monitor data freshness, drift, and model performance Apply security, privacy, and governance Chapters include tested Python and SQL code, troubleshooting guidance, key takeaways, and knowledge-check questions. The appendices provide a data dictionary, an engineering checklist, and a project code map. A glossary and index complete the book. It is written for readers who know Python and basic SQL. That includes aspiring data engineers and data scientists, as well as ML and software engineers moving into production work.

Customers reviews

More customer reviews
  • 0% (0)
  • 0% (0)
  • 0% (0)
  • 0% (0)
  • 0% (0)

Frequently Asked Questions about the Book

Is the book original?

All books in our catalog are Original.

In what language is the book written?

The book is written in English.

What is the format of this book?

The binding of this edition is Paperback.

Questions and Answers about the Book

Do you have a question about the book? Login to be able to add your own question.

Opinions about Bookdelivery

More customer reviews