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.

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.

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.