Oracle AI Data Platform brings together data, analytics, and AI in a single, governed environment. It unifies the entire data-to-AI lifecycle—from ingestion and preparation to model development and deployment—on a secure, scalable foundation. With integrated services like Autonomous AI Database, Oracle Analytics Cloud, Apache Spark and OCI Generative AI, AI Data Platform helps teams prepare, govern, and apply AI to their data efficiently. Whether you’re engineering data, developing models, or building intelligent applications, AI Data Platform provides the speed, governance, and integration needed to operationalize AI across the enterprise. In this blog, you’ll learn about what’s included in AI Data Platform and the purpose of each component in the platform.

What’s in the Box?

Each component of Oracle AI Data Platform is designed to address a specific stage of the data-to-AI value chain, providing seamless, secure, and governed experiences from ingestion to action.

AI Data Platform seamlessly integrates multiple OCI services, spanning data, AI, analytics, and governance, into a cohesive experience that enables enterprises to build, deploy, and scale AI-powered applications with consistency and ease.

AI Data Platform organizes data using a medallion architecture—bronze, silver, and gold layers—ensuring structured refinement from raw ingestion to analytics- and AI-ready assets. This layered approach is fully governed through the Unified Catalog. Data products processed to the gold layer are available for Analytics using Analytics Cloud or other visualization tools.

Data and AI Catalog
A unified repository that manages both data and AI assets across the enterprise. The catalog enables discovery, governance, and lineage, ensuring all data, models, and agents are governed through a consistent policy and metadata layer. This ensures engineers spend less time searching for data and more time building data and AI pipelines.

AI Data Platform Workbench
A unified development environment (IDE) for developers and administrators to build, manage, and deploy data and AI-driven solutions. The workbench integrates notebooks, agent development, orchestration, and catalog management within a single collaborative interface. It provides a consistent build-and-deploy experience—bridging the gap between experimentation and production.

  • Workspace – The developer Workspace within AI Data Platform Workbench is for designing, testing, and managing data products and AI agents. It provides tools for defining agent roles, permissions, and integration points.
  • Agent Flow – A visual design environment provides a no-code and pro-code approaches for connecting and orchestrating agents into coordinated flows. It allows teams to define triggers, actions, and automation paths across analytical and operational tasks.
  • Notebooks – A multi-language, AI-assisted development interface for data engineering and data science. Notebooks support experimentation, version control, and scalable execution on Spark clusters for analytics, feature engineering, and AI model development. They make it easy to iterate, share, and reproduce experiments within the same governed environment.
  • Workflow Orchestration
    A robust scheduling and automation layer for sequencing tasks such as notebook runs, model training, and agent activation. Ensures reliable and repeatable execution of data and AI workflows across dynamic compute environments. Engineers can version and repeat complex AI workflows predictably, ensuring reproducibility in production.

Autonomous AI Database
Autonomous AI Database is the analytical engine and “gold layer” of the AI Data Platform lakehouse. It delivers high-performance querying, vector storage for retrieval-augmented generation (RAG), and advanced analytics, with full governance, lineage, and security enforcement. Developers can combine structured analytics with vector search in a single query—reducing data movement and latency for RAG workloads.

Oracle Analytics Cloud (OAC)
OAC serves as the semantic and visualization layer, transforming governed, AI-ready data into actionable business insights. It leverages the unified catalog and lakehouse, providing AI-powered analytics, natural language queries, summarization, and workflow orchestration. Business teams gain explainable AI insights directly from governed datasets—closing the loop between model and decision.

OCI Generative AI
Oracle Cloud Infrastructure Generative AI supplies foundation models used by agentic applications and AI experiences. These models integrate natively with AI Data Platform’s governed lakehouse, catalog, and development tools, allowing enterprises to securely apply AI to their own data for transformative business outcomes. Engineers can easily test and deploy generative use cases without managing separate model infrastructure.

OCI AI Services
These are prebuilt, domain-specific AI capabilities—spanning language, vision, and anomaly detection—that embed intelligence into data pipelines, applications, and agents. OCI AI Services extend AI Data Platform’s environment to operationalize both custom and ready-made AI securely and at scale, giving developers instant access to proven models without rebuilding core logic.

Object Storage
OCI Object Storage acts as the backbone for all enterprise data, supporting structured and unstructured, batch and streaming data. It provides secure, scalable, and cost-effective storage using open formats like Delta and Iceberg, enabling seamless access and zero-ETL integration for analytics and AI training. This consistency in format and access means data scientists can train and retrain models faster, without re-engineering pipelines.

Security and Governance
AI Data Platform integrates end-to-end security and governance at every layer. This includes centralized enforcement of security policies, access controls, auditability, and lineage to ensure trust, regulatory compliance, and consistent oversight of all data, models, and agents.

Conclusion

AI Data Platform operationalizes the full data-to-AI lifecycle on a single, governed foundation. With a medallion architecture—refining raw data in bronze, organizing domain assets in silver, and delivering analytics and AI ready content in gold—teams gain a consistent path from ingestion to intelligence. The Unified Catalog, lakehouse storage, Autonomous AI Database, Spark, OCI Generative AI, Analytics Cloud, and workflow orchestration all work together to streamline how data is prepared, enriched, secured, and activated. Whether building data products, developing models, or delivering AI-powered insights, practitioners can move from prototype to production with confidence, repeatability, and governance built in.

Quick Start Playbook

  • Curate priority data in Object Storage and the Catalog; refine into Autonomous AI Database.
  • Prototype an agent or workflow in Notebooks using OCI Generative AI.
  • Share insights in Oracle Analytics Cloud; expose actions in Agent Hub.
  • Schedule and monitor with Workflow Orchestration under unified security and governance.

As you add more data and use cases, the same foundation scales without adding risk.

AI Data Platform is designed for practitioners who need to unify data engineering, AI experimentation, and operationalization in one environment—without compromising governance or control.

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