£45.70

Data Engineering for AI/ML Pipelines: A Comprehensive Guide to Building Scalable, Intelligent Data Workflows

AI & Machine Learning

Price data last checked 85 day(s) ago - refreshing...

View at Amazon

Price History & Forecast

Last 6 days • 6 data points (No recent data available)

Historical

Generating forecast...

Price Distribution

Price distribution over 6 days • 1 price levels

Days at Price

Price Analysis

Most common price: £46 (6 days, 100.0%)

Price range: £46 - £46

Price levels: 1 different prices over 6 days

Description

Data Engineering for AI/ML Pipelines: Build Scalable, Intelligent Workflows. The definitive guide to mastering data engineering for AI and machine learning pipelines,updated with the latest tools, real-world case studies, and proven strategies. Whether you're a data engineer, ML practitioner, or tech leader, this comprehensive book equips you to design high-performance, production-ready AI/ML pipelines that scale from prototype to enterprise. In today’s AI-driven world, data is the new oil, but only if refined through robust, intelligent pipelines. Data Engineering for AI/ML Pipelines takes you beyond theory into actionable practice, covering every stage of the modern data lifecycle with real code, and benchmarks. Why this book stands out: 12 in-depth chapters with Python code examples, PySpark workflows, and Airflow 2.10+ DAGs Up-to-date 2025 tools: Apache Kafka 3.8, Flink 2.0, dbt Core 1.9, Databricks Unity Catalog, Snowflake Cortex, Feast, and emerging AI platforms Real-world case studies from healthcare, finance, and robotics, featuring Physical AI, RAG pipelines, and real-time analytics What you’ll master: Ingest and process structured, unstructured, and streaming data at scale Automate data cleansing, feature engineering, and anomaly detection with AI Orchestrate ML workflows using Kubeflow, Prefect, and Airflow Secure pipelines with differential privacy, federated learning, and zero-trust models Optimize cost and performance with mathematical models and simulations Future-proof with edge AI, quantum-ready data models, and interoperability protocols (MCP, A2A) Includes: GitHub-ready code repos and public datasets Appendix with tool benchmarks, glossaries, and resources “The backbone of every successful AI system is a bulletproof data pipeline. This book is your blueprint.” – Dr. Alex J. Sterling Don’t let poor data engineering derail your AI initiatives. Grab your copy today and build scalable, intelligent, future-ready ML pipelines that deliver real business impact.

Product Specifications

Format: hardcover
ASIN: B0G25VJ2M9
Category: Books > Subjects > Computing & Internet > Computer Science > AI & Machine Learning
Domain: Amazon UK
Release Date: 13 November 2025
Listed Since: 13 November 2025

Barcode

No barcode data available

Data Engineering for AI/ML Pipelines: A Comprehensive Guide to Building Scalable, Intelligent Data Workflows

Price History & Forecast

Price Distribution

Price Analysis

Description

Product Specifications

Barcode

Similar Products You Might Like

Ethical AI Design for Engineers: A Comprehensive Guide to Building Trustworthy and Equitable Intelligent Systems for Engineers

The Most Complete AI Agentic Engineering System: Step-by-step guide to build, optimize, and scale LLM agents—with exclusive monthly and rigorous ... metrics, and built-in self-improvement

SCALA 3 FOR BIG DATA ENGINEERING: Functional and object-oriented programming synergy for Spark and enterprise AI

INTELIGENCIA ARTIFICIAL - GUÍA ESENCIAL (Introduction to Artificial Intelligence)

POWERED-AI BUSINESS INTELLIGENCE : FROM CONCEPTS TO IMPLEMENTATION: The Complete Guide to Transforming Your Enterprise with AI-Optimized Analytics

Mastering AI in Java: From Machine Learning to Smart Microservices

Software Engineering and Data Science

AI Unlocked : The Most Comprehensive and Easy-to-Understand Guide to Artificial Intelligence

The Data Lakehouse Revolution: Harnessing the Power of Databricks for Generative AI and Machine Learning

Modern Data Engineering: Design, Build, and Scale Reliable Data Systems for Analytics, AI, and the Cloud: 8 (Data Engineering Domain For professional)

Reshaping the Enterprise Training Model with AI: Future-Ready Solutions for Tomorrow’s Workforce (Unlocking Productivity with AI-Driven Tools)

Data Warehousing & Data Modeling for Data Engineers Designing Scalable, Analytical, and Enterprise-Grade Data Platforms (Data Engineering Domain For professional)

Foundations of AI in Electrical Engineering: From Concepts to Applications

Non-Deterministic Software Engineering: How to Build Reliable Software with AI Assistants Without Losing Quality, Security, or Control

Machine Learning Engineering Mastery: From Mathematical Foundations to Production-Grade AI Systems with MLOps, Deep Learning & Modern AI Tools

INTEGRATION OF ARTIFICIAL INTELLIGENCE INTO THE ARCHITECTURAL ENVIRONMENT: DESIGN, CONSTRUCTION, OPERATION

Data and AI Management Handbook: From Data Engineering to Product Strategy

AI-Driven Learning and Engagement in Higher Education (Advances in Higher Education and Professional Development)

Prompt Engineering with Purpose - Research and Development Book

Zero to Data Scientist: The Complete Step-by-Step Guide to Mastering Data Science, Machine Learning & AI—No Experience Needed

Zero to Data Scientist: The Complete Step-by-Step Guide to Mastering Data Science, Machine Learning & AI—No Experience Needed

IPE How to Book - Step 2: Define the Process: Process Infrastructure Deployment For the AI Era (Integrated Process Excellence℠ (IPE))

Engineering With AI

IPE How to Book - Step 1: Create a Positive Environment: Process Infrastructure Deployment For the AI Era (Integrated Process Excellence℠ (IPE))