book

A Practical Guide to Oracle AI Engineering

Name: A Practical Guide to Oracle AI Engineering
ISBN: 9781806110797

by Erik Benner, Hicham Assoudi, Tural Gulmammadov

May 2026

Beginner to intermediate

354 pages

9h 12m

English

Packt Publishing

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Preface
Who this book is forWhat this book coversTo get the most out of this bookDownload the example code filesGet the code bundleFree benefits with your bookHow to UnlockGet in touch
Overview of Oracle’s AI and ML Ecosystem
Your purchase includes a free PDF copy + exclusive extrasAn overview of Oracle’s AI stackAI Services: Pre-built intelligenceOCI Generative AI: Foundation models for the enterpriseAI Agents: Orchestrating intelligent workflowsML services: OCI Data ScienceAI infrastructure: The compute foundationOCI Data Science: Building and deploying machine learning modelsOCI Data Science model deploymentOCI Data Science pipelinesIntegration of Oracle’s AI and machine learning ecosystem for data engineeringHow the pieces connectOverview of Oracle’s cloud and database solutions for machine learningOracle Autonomous DatabaseOracle AI Database 26aiAI Vector SearchONNX model importSelect AIRAG with DBMS_VECTOR_CHAINOCI Supercluster vs. legacy SuperClusterReal-world use case examplesSummaryGet this book’s PDF version and more
Oracle AI Solution Lifecycle, Design Patterns, and Platform Choices
Oracle AI as an enterprise architectureEnterprise AI and Oracle AIOracle’s approach to enterprise AIAddressing the AI lifecycle with the Oracle AI stackNavigating enterprise realities and constraintsAI lifecycleKey design drivers across the lifecycleCore Oracle AI architecture patternsModern AI landscapeMapping AI stages to Oracle AI capabilitiesOracle design patternsIn-database AI patternsService-centric and GenAI patternsGenerative-centric and agentic AI patternHybrid AI patternsSummaryGet this book’s PDF version and more
Data Preparation and In‑Database Model Training
Introduction to Oracle Machine LearningOML lifecycle overviewData preparation within Oracle DatabaseData cleaning and transformation fundamentalsIn-database tools and techniques for data cleaningFeature engineering for predictive modelsBuilding features with Oracle SQL and PL/SQLAutomating feature engineering with Oracle toolsLeveraging in-database machine learningOML components by deployment environmentKey features of OMLCommon in-database machine learning use casesAutomating model selection and tuning with AutoMLOracle AutoML workflowBenefits of using Oracle AutoMLImplementing AI Vector Search in OracleUnderstanding and creating vector embeddingsPerforming semantic similarity searchEnhancing LLMs with RAGQuerying data naturally with Select AIBenefits of using Select AISelect AI use cases in practiceSummaryFurther readingGet this book’s PDF version and more
Model Deployment and In‑Database Management
In-database machine learning in Oracle Database Overview of OML Services for deploying models in databaseModel scoringModel inspection and explainabilityEnterprise deployment planningRESTful endpoint supportBenefits of in-database model deploymentSeamless integration with enterprise workflowsEnterprise-grade security and governanceScalability and high availabilityConsistency across the machine learning lifecycleReal-time analytics with in-database machine learningTechniques for real-time prediction SQL and PL/SQL: inline predictionsREST APIs with OML ServicesSystem-level optimizations: Exadata and Autonomous DatabaseAutomatic data preparationPartitioned models and parallel scoringGovernance, security, and observabilityReal-time machine learning use cases Managing and monitoring OML modelsManaging model versions and lifecycleMonitoring model performanceOML Services REST API SummaryFurther readingGet this book’s PDF version and more
Advanced Techniques for Optimizing Machine Learning and AI Workloads on Oracle Database
Technical requirementsScaling and optimizing in-database AI modelsData layer: partitioning, sharding, and Oracle True CachePartitioningShardingOracle True CacheIn-database feature engineering, model execution and deployment Feature engineeringModel executionModel deploymentIn-database similarity search and RAGEmbeddings and vectorsEmbeddings generationAdvanced vectorization Connecting LLMs with data using RAGRAG workflow overviewEnd-to-end RAG scenarioAdvanced RAGSummaryGet this book’s PDF version and more
Preparation and Installation of MySQL HeatWave
Technical requirementsUnderstanding MySQL and HeatWaveA quick MySQL history lessonWhat is HeatWave? Data lakeData warehouseHeatWave LakehouseHow to build a HeatWave databaseOCI tenancy access Creating a compartment and VCNAllowing network accessCreating a user group and policyBuilding a HeatWave databaseWorking with data in MySQL HeatWaveImporting dataAutomatic loadingManual loading Training on the dataHeatWave and GenAI and vectorsSummaryGet this book’s PDF version and more
Model Deployment and Optimization on HeatWave
Technical requirementsHow to build a Linux VM and load your dataBuilding a Linux VMMySQL CLI data loading Using the modelHeatWave Autopilot Use cases for machine learning inside the databaseFraud detection and anomaly detectionPredictive maintenance in manufacturingCustomer churn predictionRecommendation systemsDemand forecasting and inventory optimizationNatural language processing for customer feedbackReal-time personalization in marketingHealthcare predictive analyticsCybersecurity and access controlFinancial forecasting and risk modelingSummaryGet this book’s PDF version and more
Introduction to Generative AI Services
Technical requirementsWhat is GenAI?How GenAI worksCommon applications of GenAIUnderstanding models and core conceptsWhat are tokens?Why different models existPretrained models available in OCICohere modelsMeta Llama familyGrok modelsGoogle GeminiKey GenAI termsContent moderationEmbeddingFrequency penaltyGenAI modelInferenceLikelihoodModel endpointPlaygroundPreamblePresence penaltyPrompts and prompt engineeringRetrieval-augmented generation (RAG)Retirement and depreciationStreamingTemperatureTop KTop PGetting started with OCI Generative AIExploring models in the playgroundWhy use a dedicated AI cluster?Creating a dedicated AI cluster in OCICalling GenAI from a REST APIUnderstanding GenAI agentsHow an OCI Generative AI Agent uses SQL for retrievalBuilding a GenAI agent with RAG and a databaseSummaryGet this book’s PDF version and more
Leveraging Oracle AI Services for Machine Learning
Technical requirementsIntroduction to Oracle AI ServicesAn overview of Oracle AI ServicesBenefits of using Oracle AI Services for AI and machine learning developmentCore Oracle AI Services and use cases Preparing to use the APIsSpeech AILanguage AIVision AIDocument Understanding Implementation and best practices Calling AI services using Oracle FunctionsTips for optimizing performance, scalability, and costSecurity considerations when using AI servicesSummaryGet this book’s PDF version and more

Leveraging Oracle Data Science Service for Machine Learning
Introduction to Oracle Data Science Service OCI Data Science lifecycleKey features and benefits for machine learning development End-to-end machine learning workflowCreating and managing notebooksNotebook configuration and executionRunning models via REST APIBest practices for performance and security in OCI Data ScienceOptimizing model performance and scalabilityData qualityOptimizing compute performanceCollaboration optimizationSecurity and governance considerationsAccess control to protect the dataAuditing and security monitoringManaging regulatory compliance SummaryGet this book’s PDF version and more
Building Intelligent Applications with Oracle Digital Assistant
Oracle Digital Assistant foundationsOracle Digital Assistant overviewOracle Digital Assistant high-level architectureDigital assistants (chatbots)SkillsChannelsOracle Digital Assistant conversational applicationsOracle Digital Assistant projectOracle Digital Assistant project teamConversation design best practices, challenges, and limitationsDesign best practicesDesign challengesLimitationsExtending Oracle Digital Assistant with GenAILLM-based conversationConfiguring the LLM integrationExecuting generative inference at runtimeAgents-based conversationDesign patternsOracle Digital Assistant with GenAI agentsSummaryGet this book’s PDF version and more
Machine Learning and AI Security, Governance, and Best Practices
Ethical and transparency challenges in AI systemsSafety, security, and data risks in AI deploymentSafeguarding sensitive data during model developmentSafeguarding challenges and solutionsOrganization changes are requiredBest practices for data governance and regulatory complianceData governanceRegulatory complianceData qualityData transparencyData privacyImplementing governanceThe need for AI guardrailsSummaryGet this book’s PDF version and more
Index

Content preview from A Practical Guide to Oracle AI Engineering

10 Leveraging Oracle Data Science Service for Machine Learning

In this chapter, we will explore Oracle Data Science Service, what it is, how it works, and how you can deploy a simple use case. The chapter concludes with a set of performance and security best practices. It is highly recommended that you are already familiar with how Oracle Cloud Infrastructure (OCI) policies and user groups are managed; Chapter 6, Getting Started with MySQL HeatWave, provides a refresher, if needed.

In this chapter we will cover the following topics:

Introduction to the Oracle Data Science Service
End-to-end machine learning workflow
Best practices for performance and security in OCI Data Science

Introduction to Oracle Data Science Service

Oracle Data Science ...

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Publisher Resources

ISBN: 9781806110797

Cloud Computing

Data Engineering

Data Science

AI & ML

Programming Languages

Software Architecture

IT/Ops

Security

Design

Business

Soft Skills

A Practical Guide to Oracle AI Engineering

by Erik Benner, Hicham Assoudi, Tural Gulmammadov

10

Leveraging Oracle Data Science Service for Machine Learning

Introduction to Oracle Data Science Service

Become an O’Reilly member and get unlimited access to this title plus top books and audiobooks from O’Reilly and nearly 200 top publishers, thousands of courses curated by job role, 150+ live events each month,
and much more.