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Data Engineer's Guide to Oracle Machine Learning and GenAI Services: Modern data engineering practices for creating efficient, AI-driven applications at enterprise scale

Data Engineer's Guide to Oracle Machine Learning and GenAI Services: Modern data engineering practices for creating efficient, AI-driven applications at enterprise scale

          
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About the Book

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 Data Engineer’s Guide to Oracle Machine Learning and Gen AI Services, 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.

Table of Contents:
Table of Contents

  1. Overview of Oracle's AI and ML Ecosystem
  2. AI Infrastructure on OCI
  3. Tools and Frameworks for Model Development on OCI
  4. Model Deployment, Optimization, and Specialized Services on OCI
  5. Data Preparation and In-Database Model Training
  6. Model Deployment and In-Database Management
  7. Advanced Techniques for Optimizing ML on Oracle Database
  8. Data Preparation and Training on MySQL HeatWave
  9. Model Deployment and Optimization on MySQL HeatWave
  10. An Introduction to GenAI Services
  11. Utilize Oracle AI Services for Machine Learning
  12. Leveraging Oracle Data Science Service for Machine Learning
  13. Building Intelligent Applications with Oracle Digital Assistant
  14. Machine Learning Security, Governance, and Best Practices


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Product Details
  • ISBN-13: 9781806110797
  • Publisher: Packt Publishing Limited
  • Publisher Imprint: Packt Publishing Limited
  • Height: 235 mm
  • Sub Title: Modern data engineering practices for creating efficient, AI-driven applications at enterprise scale
  • ISBN-10: 1806110792
  • Publisher Date: 08 May 2026
  • Binding: Paperback
  • Language: English
  • Width: 191 mm


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