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• Course · FoundationOCI Foundations AssociateConcepts of cloud, identity, network, compute and storage at OCI.
• Course · FoundationOCI AI Foundations AssociateAI vocabulary, autoLearning, Generative models and OCI services.
• Course · FoundationOracle Agentic AI Foundations AssociateAgents, tools, MCP, OCI Enterprise AI and Oracle AI Database.
• Course · AI & AgentsOCI Generative AI ProfessionalGenerative models and their applications in OCI.
• Course · AI & AgentsOCI Data Science ProfessionalData, models and life cycle of automatic learning solutions.
• Course · AI & AgentsOCI Enterprise AI ProfessionalConstruction and operation of AI solutions for companies.
• Course · Data & AIOracle Autonomous AI Database ProfessionalOperation and Autonomous AI Database capabilities.
• Course · Data & AIOracle AI Vector Search ProfessionalVectors, similarity and information search for AI.
• Course · Data & AIAgentic AI for Oracle AI Database ProfessionalAgents that tap data and capabilities from Oracle AI Database.
• Course · Data & AIOracle AI Data Platform ProfessionalData ruled and agent design, testing and deployment at AI Data Platform. 1Z0-1154-26 exam.
• Course · ArchitectureOCI Architect AssociateOCI solutions with identity, network, compute and storage.
• Course · GovernanceData Management FundamentalsData Management Foundations with DAMA and Oracle Cloud: 11 areas and cases applied.
• Video · CertiTubeOCI Object Storage: Copy Files From Windows With RcloneHow can a Windows folder be copied to an OCI Object Storage bucket with rclone? The tutorial prepares rclone on Windows and creates a remote through the Object Storage S3-compatible API. Customer Secret Keys are generated from the OCI user profile, and Access Key, Secret Key, region, endpoint and namespace are configured for the connection. Files are then copied from a local folder to an existing bucket and checked in the console. A new file is added to the folder, and the copy is repeated to verify its transfer. The walkthrough ends by showing access to the rclone web interface for execution and configuration review. The example focuses on Windows file copying through the configured S3 remote.
• Video · CertiTubeOCI Data Flow: Build And Deploy PySpark Apps With Code EditorHow can a PySpark application be developed in the browser and run in OCI Data Flow? The tutorial prepares OCI authentication and Spark in Cloud Shell, creates a Code Editor project and uses Spark Oracle Datasource to read an Oracle Autonomous Database table. The wallet is stored in OCI Object Storage, and the application configures database connection details. The script is first tested in Cloud Shell with Run Locally, checking the first five records. The Python artifact is then uploaded to Object Storage, and a Data Flow application is created to run it on managed resources. Resource Principal is configured for OCI resource access while the example database credentials remain in use. After the Data Flow execution, Spark UI stages and the DAG are reviewed to understand job dependencies.
• Video · CertiTubeGenerative AI & LLMs: From Neural Networks To Token PredictionHow are neural networks related to text generation with an LLM? The introduction uses buying decisions and review classification to explore inputs, weights, thresholds and activation functions. It starts with a simple perceptron and moves to the multilayer perceptron (MLP), embeddings, hidden layers, ReLU and softmax. Multiclass classification is used as a teaching analogy for a distribution over possible tokens, without equating the example MLP with a complete language model architecture. A Shakespeare question illustrates gradual response construction and introduces the end-of-sequence token. The walkthrough provides a conceptual basis for exploring OCI Generative AI. It focuses on understanding neural networks and token prediction, with references for further study.
• Video · CertiTubeAI Quick Actions: Data Science: Fine-TunedHow can CodeLlama 13B be adapted to SQL and PL/SQL examples with AI Quick Actions? The demonstration uses OCI Data Science to prepare a JSONL dataset, select a base model and configure fine-tuning. Files and results are stored in a versioned OCI Object Storage bucket. Model version, GPU infrastructure, network and parameters such as epochs are selected. The job is monitored, model provenance and parameters are reviewed, and an endpoint is deployed. Python invocation includes Server-Sent Events support and authentication through ADS and Resource Principal. Test Your Model uses a question from the training dataset itself to inspect the generated SQL and column names. An extra code-based implementation converts JSONL to CSV and separates training and chat applications. The narrator explains that this final test response still needs adjustment.
• Video · CertiTubeOracle Select AI + OpenAI: Chat With Your Data In Oracle APEXHow can Oracle Autonomous Database data be queried from an APEX chat? The demonstration integrates Select AI and OpenAI for natural language questions, generated SQL and query explanations. Database Actions creates a user and prepares network access and execution permissions for DBMS_CLOUD and DBMS_CLOUD_AI. An OpenAI credential is then configured using an API Key, along with an AI profile containing the target tables. The test asks how many customers in San Francisco are married and reviews the corresponding SQL query. The walkthrough creates an Oracle APEX workspace associated with the demo schema, imports the example SQL application and installs its supporting objects. The chat repeats the question and explores options to display SQL and its explanation.
• Video · CertiTubeOCI Data Science: Import And Deploy Hugging Face Models With AI Quick ActionsHow can a Hugging Face model be imported and deployed for inference in OCI Data Science? The demonstration uses AI Quick Actions to register a model by its identifier, store its files in OCI Object Storage and deploy it on NVIDIA GPU infrastructure. A versioned bucket is created, and the Data Science terminal authenticates to Hugging Face through its CLI and an access token. The model, container, bucket and target folder are then selected. Deployment configuration chooses the infrastructure and keeps default container arguments. Once available, the model is queried with parameters, and invocation options for CLI, Python and Java are reviewed. The walkthrough ends by checking model files in Object Storage, where they remain available for other deployments.
• Video · CertiTubeOracle Autonomous Database 23ai: Build A RAG Chatbot With AI Vector SearchHow can customer data become context for a RAG chatbot? The example connects Oracle Autonomous Database 23ai, AI Vector Search and OCI Generative AI. It starts with customer, plan, contract, billing and satisfaction tables. Views prepare text and metadata; the sp_elt_insert_into_docs procedure splits the content, generates embeddings and stores it in DOCS for retrieval. The walkthrough reviews DBMS_VECTOR, DBMS_VECTOR_CHAIN, UTL_TO_CHUNKS and JSON_TABLE, along with industry, case and view parameters. It prepares access policies, Autonomous and its wallet, an API Key, a VCN and a Windows OCI Compute instance. The chatbot is then installed; NGINX is presented as an optional configuration. The demo compares selected profiles and cases to observe changes in retrieved context. It also generates test customers, updates their embeddings and queries the information through the chat. The walkthrough ends by inspecting DOCS, metadata, views and the procedure in Autonomous.
• Video · CertiTubeOracle AI Copilot: Real-Time Support With Speech, Sentiment & Generative AIHow can a customer service team be supported during a customer conversation? This Smart Co-Pilot combines OCI Speech for live transcription, OCI Language for sentiment analysis and OCI Generative AI for language models and embeddings. Oracle Autonomous Database 23ai with AI Vector Search provides data and context; OCI Compute hosts the application and chatbot. The interface brings together contract information, call transcription, sentence-level sentiment, aspects such as service or customer care, and suggested responses. The assistant uses this context to support the team during the interaction. The walkthrough covers requests, complaints and customer analysis. The request example concerns a move from prepaid to postpaid and available promotions. The copilot suggestions help the team respond and guide customer service.
• Video · CertiTubeOracle Autonomous Data Warehouse: Data Quality & Workflow FrameworkHow can transformations, data quality and dependencies be organized from staging to the dataset? This Data Framework on Oracle Autonomous Data Warehouse draws on DAMA data management practices. Its Data Staging Area (DSA) classifies information as STG for transactional data, STC for catalog tables and STH for historical data. Query Mapping produces reusable intermediate tables. The design includes indexes, parameters in tables and business rule checks before each mapping, with email error notifications. Metadata documents attributes and supports versioning and change management; preserving history through partitions is also proposed. The output dataset comes from the final mapping and its Query Dataset. QuickDBD is presented to support dataset modeling. Each workflow is encapsulated in a stored procedure with one or more mappings and generates one dataset. The monitor records checks, mappings, datasets, start and end, retaining the last N executions. Dependencies allow other workflows to be triggered as a controlled execution network.
• Video · CertiTubeOCI Speech: Convert Audio To Text With Oracle AIHow can a voice recording be converted to text for analyzing a conversation? The test presents an outdoor recording with surrounding sounds to explore speech recognition in a customer service context. The presenter explains audio-to-text conversion and its role as a starting point for analyzing conversations with artificial intelligence. The narration also discusses identifying speakers and working with languages in conversations involving several people. The resources link to OCI Speech documentation and the Oracle AI Accelerator project for exploring integration.
• Video · CertiTubeOracle AI Accelerator: 5 Demos with Select AI, RAG & Multimodal AIHow can you explore five ways to query tables, documents, audio and images from one application? Oracle AI Accelerator combines Oracle Autonomous Database, files in OCI Object Storage and OCI Generative AI models. The walkthrough prepares the environment, connection, credentials and users with their modules before starting the tests. The five demos cover Select AI over a Titanic CSV file; Select AI with RAG over HTML and TXT files; OCR and PDF questions with OCI Document Understanding; audio transcription to SRT with OCI Speech; and JPG document processing and image queries with a multimodal model. The transcribed audio is then analyzed with a generative model to summarize conversations, answer questions and explain sentiment analysis. The walkthrough also reviews generated SQL, column comments, response sources, metadata and embeddings stored in Autonomous, along with application history and metrics.
• Video · CertiTubeLangGraph + Oracle Select AI: Agents, Workflows & RAGHow can you query tables and documents with LangGraph agents and workflows? The campaign management example uses eight tables covering offers, segments, channels and interactions. CSV files are loaded, and column comments provide context for Select AI. Loan documents in PDF format are ingested with OCI Document Understanding and prepared for RAG using LangChain and Oracle AI Vector Search. The demonstration compares four LangGraph patterns. An agent selects between Select AI and RAG according to the query. The walkthrough then tests parallel execution of both tools, configured routing and an orchestrator that assigns predefined tasks and gathers results through a synthesizer. Loan offers are queried, and the generated SQL is copied and checked in Autonomous. Content is also retrieved from the PDF documents. The walkthrough brings together relational data and documents centralized in Oracle Autonomous Database, with assistance from OCI Generative AI.
• Video · CertiTubeCreate and Activate an Oracle Cloud AccountHow can you create and activate an Oracle Cloud account and protect access with MFA? The video walks through Oracle Cloud Free Tier registration: personal details, email verification, password, account name, region, and card verification. It then shows activation and the first sign-in to the OCI console. Oracle Mobile Authenticator is linked using a QR code to configure multifactor authentication. The closing section shows a short test of the OCI Generative AI chat in Chicago with a Grok model.
• Video · CertiTubeOracle Cloud Data Governance: DAMA, Metadata & AI ArchitectureHow can data governance connect architecture decisions with business goals? The talk applies DAMA-DMBOK concepts to a hypothetical case involving document and structured-data ingestion and transformation in Oracle Cloud. It connects an architecture using OCI Object Storage, OCI Events, OCI Functions, and Oracle Autonomous Database with security, quality, metadata, and traceability. Responsibilities, coordination between departments, and isolated sources are reviewed. An illustrative nine-month roadmap supports a discussion of migration, coexistence, standardization, early value delivery, and TCO and ROI assessment, including operational effort. The walkthrough introduces Database Actions and Data Studio, a comparison of cloud platforms, and examples involving embeddings, medallion architecture, and workflows. It ends with questions about process dependencies and learning to work as a data architect.
• Video · CertiTubeOracle AI: Batch PDF Processing With OCR & AI AgentsHow can you process a batch of PDF files with OCR and AI agents and track the progress of each document? The demonstration walks through a desktop utility with a batch of 1000 ZIP files containing PDF documents. The input folder is selected, an application token is obtained, and a workflow starts by performing OCR before sending each document to the agent queue. A JSON output is generated when processing is complete. The demonstrated configuration supports two simultaneous OCR tasks and up to eight agent tasks in parallel. The panel shows status, pages, times for each stage, and estimated progress. It also lets users save API activity logs, export the table to CSV, and reprocess a document. The video explains configurable retries and the relationship between concurrency and infrastructure in Kubernetes. The limits shown apply to this example configuration.
• Video · CertiTubeOracle AI Database 26ai: Build Agents With Select AI, SQL & RAGHow can an agent team query tables and documents in Oracle Autonomous AI Database 26ai? The demonstration uses Oracle AI Accelerator to prepare two sources: a debt-collection dataset in CSV format and a public financial statement from Banco de Chile in PDF format. A group and user are created, the modules are enabled, and the column dictionary and AI profiles are reviewed. Select AI queries the requested amount grouped by status. Select AI with RAG retrieves information from the PDF. The demonstration then reviews the team, agent, task, and SQL and RAG tools in DBMS_CLOUD_AI_AGENT, and tests a query using that workflow inside the database. The closing section discusses possible future automation using functions, notifications, and other tools, and introduces Oracle AI Data Platform. These possibilities are presented as extensions of the example.
• Video · CertiTubeOracle AI Database Private Agent Factory: Setup & Your First AI AgentCreate your first agent with Oracle AI Database Private Agent Factory from deployment in OCI up to a test that turns a PDF into a blog article. On this tour we connect the platform with Oracle Autonomous AI Database 26ai and we set up models using OCI Generative AI. We have prepared the share, the permissions, the database user and the connecting wallet. Then we set up the network VCN and we have deployed the application from OCI Marketplace about a virtual machine OCI Compute. We continue with initial user creation, the connection test with Autonomous and setting up a generative and another model of embeddings. We checked the endpoint and authentication with API Key of OCI to set up connections and save model settings. Finally, we have been exploring the templates and visual environment of Agent Builder. A from a template, we check the prompt, we select the LLM and we have tested a flow that generates a blog article from the contents of a PDF. We then compare the result with the original document to review how you use the input information.
• Video · CertiTubeOracle AI Data Platform Workbench: X Sentiment Analysis With OCI Language & Agent FactoryConvert X's comments to data that you can discuss with SQL and consult from an agent. The demonstration integrates Oracle AI Data Platform Workbench, OCI Language, Oracle Autonomous AI Database and Agent Factory. The service, workspace and cluster are created, bookstores are established and connections are prepared. A Python notebook draws answers to an X post and gets his feeling scores with OCI Language. The results move from panda to an Apache Spark DataFame and are stored in a Delta table. The categories and field toxicity_score of the notebook are computed by rules about negative feeling score. have an indicative range. Autonomous is then registered as an external catalogue and data are charged using MERGE by response identifier. The notebook is organized into a job with execution logs and an automatic program. At the end, Agent Factory connects to the database to answer questions about comments in natural language. The responses are contrasted with SQL consultations to follow the path from extraction to conversational consultation.
• Video · CertiTubeAI Document Analyzer + OCI Document Understanding V2.0: key-value extraction and routingConvert document to carne-value data and use the results to conduct your processing. In this video we integrate AI Document Analyzer with OCI Document Understanding V2.0 to identify documentary types and apply a specific extraction model to each. Generative extraction allows defining every key, its dataset and a description of the field in JSON. That's how it's adapted to different formats and to multilingual content in the languages supported by the service. In the shown flow, the first stage analyzes the first page to distinguish minutes, contracts and prospectuses. The model returns a label. a condition of workflow relates to the original file and chooses the branch and the extractor. Then, the dedicated model processes the pages and gets their keys and values. We reviewed the JSON of the generative model at OCI Object Storage, AI Document Analyzer's installation with Oracle Autonomous Database, model creation and workflow design. Finally, we have been looking at the executions and monitoring module to check the classification and results of each document.
• Video · CertiTubeAI Document Analyzer: Agentic RAG for PDFs on Oracle CloudAsk questions to your documents and find answers with evidence and citations. AI Document Analyzer combines PDFs processing, information retrieval and response generation with AI at Oracle Cloud. The application allows to load documents, process them individually or in batches and remove text per page, metadata and embeddings. You can also add metadata from CSV files to give context to queries. His flow of Agenic RAG, orchestrated with LangGraph, classifies his consultation, deletes his documentary reach, retrieves evidence and builds his response. The citations allow them to contrast what the system responds with the recovered sources. The project integrates Oracle Autonomous AI Database, OCI Object Storage and OCI Generative AI to work with the document and generate responses based on available content. It's a community and independent Oracle project. In the repository you will find the code and configuration instructions and deployment.
• Video · CertiTubeOracle AI Data Platform: Medallion Architecture, Governance & AnalyticsTake your Bronze data to Gold and connect them with analysis and governance at Oracle Cloud. In this video I show you how to combine OCI Object Storage, Oracle AI Data Platform Workbench, OCI Data Catalog and Oracle Analytics Cloud into a makehouse architecture with medallion layers. We will go through the buckets, Master Catalog, tables Delta and the organisation of Workspace. We review computer configuration, Spark UI metrics and workflows orchestration with parameters to process specific dates and work with incremental or complete loads. We also explore audit, login and access roles. We have been trying three useful cases: to add a column to a Delta table by keeping historic data, mask information from customers with OCI Language or regular expressions, and run SQL consultations about the Gold Layer to get reports. Then we organize the business context with a glossary at OCI Data Catalog and connect Oracle Analytics Cloud with AIDP to select tables, link data and prepare visualities. Thus you can follow the path from storage and transformation to analytical consumption.
• Video · CertiTubeAI Data Platform for God's Eye ViewHow to gather Social Media publications and sensor data to understand what's going on in an area? In this demo I show you how to expand God's Eye View with Oracle AI Data Platform to analyze events about a map and to check his evidence with an assistant AI.
• Video · CertiTubeAI Data Platform Workshop for VS Code CommunityDo you want to work with Oracle AI Data Platform from VS Code? At this visual demo, with no narrative, I propose a community extension to connect your editor with AIDP Workbench and work with remote notebooks. The extension allows you to explore workspaces, folders, notebooks and archives, edit remote-saved content and run Python, SQL and Scala cells using Jupitter. It also offers a Panel to check the state of the clusters and to start or stop the existing computer.
• Video · CertiTubeAI Data Platform Workshop: Data LineageWhere did your data come from and what processes transformed them before they reached a final table? In this demo, I'll show you the data lineage at Oracle AI Data Platform Workbench with the Telco Customer 360 Lineage. The lab collects CRM data, prepaid lines and recharges, after-paid bills and bills and home services. Its flow organises information in Landing, Bronze, Silver and Gold layers to build a Customer 360 view, a service portfolio and a geographical overview. Data Lineage at AIDP allows to explore the relationships between data artifacts and their generating processes. You can track upstream sources, identify downstream consumers and track the origin and transformation of columns into supported artifacts. That helps to understand dependencies and evaluate what results could be affected by a change. The demo uses the telco_lineage lab of the OCI AI Data Platform Workbench Starter Kits repository. The official documentation identifies the Lineage functionality as Preview.
• Video · CertiTubeOracle Select AI: Analyzes Your Data with Natural LanguageAsk questions about your data and get answers through SQL queries. In this video I give you Select AI Analytics deployed: an application to explore information about Oracle using Select AI. The project allows to load CSV files or to register existing tables with SELECT access. A from a question, generates SQL with Select AI, valids that the query be read only and returns an answer, rows of results and data for charts. The application integrates Oracle Autonomous AI Database with generative AI capabilities. Select AI uses program metadata to help model convert questions to SQL, bringing data analysis closer to those who prefer to start with a business question.
• Video · CertiTubeAI Data Plform Cloud Migration Starter KitsHe moves a medallion architecture lab into OCI and starts to work with an industry model at Oracle AI Data Platform Workbench. In this video we will tour the deployment with OCI Deploy Studio and access to the practice environment.
• Library · CertiRSSAutonomous AI Database ServerlessAutonomous AI Database is an autonomous and user-friendly database that scale elasticously, offers quick viewing performance and does not require database management. A fully elastic service can grow or reduce. With Autonomous AI Database Serverless, we do not have to set up or manage hardware or install software. The service takes care of the provisioning, backups, patch applications and database updates.
• Library · CertiRSSAI GenerativeGenerative AI is AI's corporate Oracle (Enterprise AI Platform) platform to create, deploy and govern AI applications at scale. It allows them to use pre-trained models or to import their own models, connect agents to corporate data and deploy applications to managed infrastructure. The platform combines tools, memory, information retrieval and isolated sandboxes to support workflows with corporate access controls, networks and model performance. Your resources includes tools for building and deploying vector agents and warehouses with Enterprise AI functions as well as tools for developers and tutorials.
• Library · CertiRSSIntegration with LangChainOCI Generative AI, a provider of big language models (LLM), integrates with LangChain. LangChain's integrations are adapted to third party services. LangChain connects LLM with other data sources and tools to facilitate the development of applications. Install `langchain-oci` with `pip install -U langchain-oci`. If you have `langchain-community` installed, that package is outdated and we recommend to install and use the latest version of `langchain-oci`. examples and use remains the same. Migration arises to continue to get updates and support. `langchain-oci` supports OCI Generative AI's chat and embeddings models. It also supports `VLLM`, `TGI` and custom endpoints with OCI Data Science Model Deplate; includes optimized implementations, improved bug management and documentation with examples and user guides. LangChain's integration of OCI Generative AI is supported for Python The configuration instructions and examples are in the LangChain Integration with OCI Generative AI documentation. LangChain4j is Java version of LangChain, created by the Java Developer Community. his API and examples are contained in LangChain4j documentation for OCI Generative AI and GitHub.
• Library · CertiRSSUse Select AI to interacted with Your Natural Language Data BaseSelect AI allows to gain access to artificial and generative intelligence by SQL with big language models (LLMs) and embeddings models. A from prompts in natural language, can generate, run and explain inquiries SQL, production text responses, chatting with a LLM, use retrieval augmented generation (RAG) with vector stors to incorporate current data and generate synthetic data. Can you use Select AI directly at SQL and PL/SQL or integrate them into applications written at Python and Java, Autonomous AI Database and Oracle AI Database. The production and production of AI are created and administered with the package `DBMS_CLOUD_AI`; The keyword `AI` in a judgment `SELECT` allows to interact with the database by prompts in natural language. To start with, check the prerequisites and tasks necessary to use Select AI. You can also explore its integration with different providers of AI supported to generate, run and explain SQL from prompts and / or chat with a LLM.