A Practical Guide to Oracle AI Engineering
Build intelligent apps with machine learning and AI across cloud and on-premises environments
Published on 29. May 2026
Book
Paperback/Softback
354 pages
978-1-80611-079-7 (ISBN)
Description
Learn how to build scalable data pipelines, train and deploy ML models, and deliver intelligent applications by leveraging the full power of Oracle's machine learning and GenAI services across cloud and database ecosystems.
Key Features
Apply practical data engineering methods to create intelligent enterprise applications
Master in-database ML, vectors, RAG, and GenAI agents through real-world examples
Learn about the ethics and security implications of AI technology
Purchase of the print or Kindle book includes a free PDF eBook
Book DescriptionIn A Practical Guide to Oracle AI Engineering, you'll learn how to tackle the challenges of building scalable, high-performance AI workflows in modern enterprises. Many organizations struggle to turn raw data into actionable insights while maintaining security, compliance, and operational efficiency. This book provides practical, end-to-end guidance for data engineers and architects to design, secure, implement, and optimize ML and GenAI solutions across Oracle Cloud, Oracle Database, and MySQL HeatWave.
Written by multiple Oracle experts with deep experience in Oracle technologies and enterprise data platforms, this book walks you through real-world examples and hands-on workflows, from data preparation and in-database ML to deploying GenAI-powered applications and intelligent agents. You'll gain skills in building pipelines, managing models, leveraging vector search for advanced AI use cases, and integrating AI into business applications with APEX and Oracle Digital Assistant. Advanced topics include scalable model deployment, serverless inference, monitoring, and MLOps best practices.
By the end, you'll be equipped to solve complex data challenges, accelerate AI adoption, and deliver measurable business impact through intelligent, production-ready solutions.What you will learn
Build scalable data pipelines for AI and ML workflows
Prepare and engineer data efficiently for in-database ML
Train, optimize, and deploy ML models across Oracle platforms
Use GenAI and RAG-enabled GenAI agents for intelligent applications
Integrate AI vector search for semantic retrieval and recommendations
Implement ML inside the database, for improved performance and data currency
Enhance business applications with AI using APEX and Oracle Digital Assistant
Apply best practices for MLOps, monitoring, and secure AI workflows
Who this book is forThis book is for data engineers, architects, IT specialists, and data leaders responsible for building, managing, and optimizing enterprise data solutions. If you face challenges in designing secure, scalable pipelines or deploying ML and GenAI applications, this guide provides practical workflows and real-world strategies to accelerate AI adoption.
Key Features
Apply practical data engineering methods to create intelligent enterprise applications
Master in-database ML, vectors, RAG, and GenAI agents through real-world examples
Learn about the ethics and security implications of AI technology
Purchase of the print or Kindle book includes a free PDF eBook
Book DescriptionIn A Practical Guide to Oracle AI Engineering, you'll learn how to tackle the challenges of building scalable, high-performance AI workflows in modern enterprises. Many organizations struggle to turn raw data into actionable insights while maintaining security, compliance, and operational efficiency. This book provides practical, end-to-end guidance for data engineers and architects to design, secure, implement, and optimize ML and GenAI solutions across Oracle Cloud, Oracle Database, and MySQL HeatWave.
Written by multiple Oracle experts with deep experience in Oracle technologies and enterprise data platforms, this book walks you through real-world examples and hands-on workflows, from data preparation and in-database ML to deploying GenAI-powered applications and intelligent agents. You'll gain skills in building pipelines, managing models, leveraging vector search for advanced AI use cases, and integrating AI into business applications with APEX and Oracle Digital Assistant. Advanced topics include scalable model deployment, serverless inference, monitoring, and MLOps best practices.
By the end, you'll be equipped to solve complex data challenges, accelerate AI adoption, and deliver measurable business impact through intelligent, production-ready solutions.What you will learn
Build scalable data pipelines for AI and ML workflows
Prepare and engineer data efficiently for in-database ML
Train, optimize, and deploy ML models across Oracle platforms
Use GenAI and RAG-enabled GenAI agents for intelligent applications
Integrate AI vector search for semantic retrieval and recommendations
Implement ML inside the database, for improved performance and data currency
Enhance business applications with AI using APEX and Oracle Digital Assistant
Apply best practices for MLOps, monitoring, and secure AI workflows
Who this book is forThis book is for data engineers, architects, IT specialists, and data leaders responsible for building, managing, and optimizing enterprise data solutions. If you face challenges in designing secure, scalable pipelines or deploying ML and GenAI applications, this guide provides practical workflows and real-world strategies to accelerate AI adoption.
More details
Language
English
Place of publication
Birmingham
United Kingdom
Target group
Professional and scholarly
Dimensions
Height: 235 mm
Width: 191 mm
Thickness: 19 mm
Weight
661 gr
ISBN-13
978-1-80611-079-7 (9781806110797)
Copyright in bibliographic data and cover images is held by Nielsen Book Services Limited or by the publishers or by their respective licensors: all rights reserved.
Schweitzer Classification
Persons
Erik Benner is the VP of Enterprise Transformation and an Oracle ACE director. He is an expert strategist for customers across the United States. His customer engagements range from enterprise cloud transformations to data center consolidation and modernization. He frequently presents at conferences such as Oracle CloudWorld, ASCEND, BLUEPRINT 4D, and FOSSY. Having worked with Oracle and Sun Systems since the mid-90s, Erik is well-versed in most of the core Oracle technologies, including Oracle Cloud, Oracle Linux, and Oracle Database. When not flying to far points of the country from the Metro Atlanta area, he enjoys spending time with his family at their observatory, where the telescopes outnumber the people. Hicham Assoudi is an AI researcher and senior Oracle technical practitioner with long-standing experience building Oracle-based solutions and implementing enterprise Oracle applications. He holds a PhD in Computer Science specializing in artificial intelligence and natural language processing, bringing a blended academic and hands-on engineering profile. His work includes designing and implementing AI-enabled architectures on Oracle Cloud Infrastructure, including GenAI and agentic AI. He is a published author on implementing AI solutions on Oracle platforms, drawing directly from real-world system design and delivery experience. Tural Gulmammadov is the Founder and CEO of Mergen AI, where he builds large-scale AI systems that execute regulatory workflows for food and beverage manufacturers. Previously, he served as Head of Core Generative AI development team at Oracle, leading teams of data scientists and machine learning engineers responsible for production-grade generative and applied AI systems. He is a former Instructor of AI/ML Ops at the University of Toronto in collaboration with the Vector Institute, where he taught how to design and build reliable machine learning systems. His technical interests center on the application of graph theory, discrete mathematics, and probabilistic reasoning to ML systems operating in distributed computing environments.
Content
Table of Contents
Overview of Oracle's AI and ML Ecosystem
Oracle AI Solution Lifecycle, Design Patterns, and Platform Choices
Data Preparation and In-Database Model Training
Model Deployment and In-Database Management
Advanced Techniques for Optimizing Machine Learning and AI Workloads on Oracle Database
Preparation and Installation of MySQL HeatWave
Model Deployment and Optimization on HeatWave
Introduction to Generative AI Services
Leveraging Oracle AI Services for Machine Learning
Leveraging Oracle Data Science Service for Machine Learning
Building Intelligent Applications with Oracle Digital Assistant
Machine Learning and AI Security, Governance, and Best Practices
Overview of Oracle's AI and ML Ecosystem
Oracle AI Solution Lifecycle, Design Patterns, and Platform Choices
Data Preparation and In-Database Model Training
Model Deployment and In-Database Management
Advanced Techniques for Optimizing Machine Learning and AI Workloads on Oracle Database
Preparation and Installation of MySQL HeatWave
Model Deployment and Optimization on HeatWave
Introduction to Generative AI Services
Leveraging Oracle AI Services for Machine Learning
Leveraging Oracle Data Science Service for Machine Learning
Building Intelligent Applications with Oracle Digital Assistant
Machine Learning and AI Security, Governance, and Best Practices