A Practical Guide to Oracle AI Engineering: Build intelligent apps with machine learning and AI across cloud and on-premises environments

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 Description

In 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 for

This 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.

Table of Contents

1. Overview of Oracle's AI and ML Ecosystem
2. Oracle AI Solution Lifecycle, Design Patterns, and Platform Choices
3. Data Preparation and In-Database Model Training
4. Model Deployment and In-Database Management
5. Advanced Techniques for Optimizing Machine Learning and AI Workloads on Oracle Database
6. Preparation and Installation of MySQL HeatWave
7. Model Deployment and Optimization on HeatWave
8. Introduction to Generative AI Services
9. Leveraging Oracle AI Services for Machine Learning
10. Leveraging Oracle Data Science Service for Machine Learning
11. Building Intelligent Applications with Oracle Digital Assistant
12. Machine Learning and AI Security, Governance, and Best Practices

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.