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Certified Enterprise Generative AI Systems on Google Cloud
Published 9/2026
MP4 | Video: h264, 3840x2160 | Audio: AAC, 44.1 KHz, 2 Ch
Language: English | Duration: 70h 38m | Size: 59.44 GB
Design secure, scalable, reliable, and governed RAG and agentic AI architectures with Gemini and Google Cloud
What you'll learn
Design complete enterprise Generative AI architectures on Google Cloud.
Build secure and scalable applications using Gemini and Vertex AI.
Develop production-ready Retrieval-Augmented Generation systems.
Create embeddings, vector indexes, semantic search, and hybrid retrieval solutions.
Build intelligent agents using Agent Development Kit and Agent Runtime.
Design sequential, parallel, hierarchical, and multi-agent workflows.
Connect AI systems to BigQuery, Cloud Storage, databases, APIs, and enterprise applications.
Process documents using Document AI, chunking, enrichment, and embedding pipelines.
Deploy AI applications using Cloud Run, GKE, Compute Engine, and event-driven services.
Protect prompts, responses, tools, and enterprise data using Model Armor and IAM.
Implement monitoring, tracing, evaluation, audit logging, and AI observability.
Apply DevOps, infrastructure as code, CI/CD, cost optimization, and disaster recovery.
Design responsible, governed, and compliant enterprise AI platforms.
Complete a capstone architecture combining Gemini, RAG, agents, security, and automation.
Requirements
Basic understanding of cloud computing concepts is helpful but not required.
Familiarity with Google Cloud is beneficial, but beginners can follow the course.
Basic programming knowledge in Python is recommended for hands-on exercises.
A computer with a modern web browser and reliable internet connection.
A Google account for accessing Google Cloud services.
Access to a Google Cloud account or trial environment for completing labs.
Basic familiarity with APIs, databases, and command-line tools is helpful.
No previous experience with Gemini, RAG, vector databases, or AI agents is required.
A willingness to learn through architecture examples and hands-on activities.
Description
This course contains the use of artificial intelligence.
Build the skills required to design, secure, deploy, and operate production-readyenterprise generative AI systems on Google Cloud. This comprehensive, hands-on course takes you beyond basic AI demonstrations and teaches you how to architect reliableRAG applications, intelligentAI agents, multimodal experiences, and governed enterprise AI platforms usingGemini,Google Cloud, and modern cloud architecture practices.
You will begin by exploring the completeGoogle Cloud Generative AI architecture, including the user, application, AI, data, integration, security, and operations layers. You will follow an AI request from the user interface through APIs, application services, models, enterprise data sources, retrieval systems, agent tools, monitoring platforms, and business actions.
The course provides practical coverage ofGemini models,Model Garden,Gemini Enterprise Agent Platform, theAgent Development Kit, Agent Runtime, Agent Gateway, agent memory, tool calling, and multi-agent orchestration. You will learn how to create sequential, parallel, hierarchical, planner-executor, router, and human-approved agent workflows for real-world enterprise use cases.
A major focus of the course isRetrieval-Augmented Generation on Google Cloud. You will learn how to ingest enterprise documents, process files withDocument AI, create intelligent chunking strategies, generate embeddings, build vector indexes, implement hybrid search, apply metadata filters, rerank results, and generate grounded responses with citations. You will compareVector Search, Agent Retrieval, AlloyDB, BigQuery, Cloud SQL, Spanner, and Firestore for enterprise retrieval workloads.
You will also design scalable application platforms usingCloud Run,Google Kubernetes Engine, Compute Engine, Cloud Functions, Eventarc, Pub/Sub, Dataflow, Workflows, and Application Integration. The course explains how services such asCloud Load Balancing,Cloud Armor,Apigee, API Gateway, Cloud CDN, and Cloud DNS protect and scale enterprise AI applications.
Security and governance are integrated throughout the course. You will implementModel Armor, prompt-injection protection, sensitive-data detection, IAM, least-privilege access, VPC Service Controls, Private Service Connect, encryption, secrets management, audit logging, compliance controls, and responsible AI review processes. You will also learn how to secure agent-to-tool communication and require human approval for high-risk actions.
Production AI systems must be observable and measurable. You will useCloud Monitoring, Cloud Logging, Cloud Trace, distributed tracing, evaluation datasets, retrieval metrics, groundedness measurements, safety evaluations, regression testing, and release quality gates. You will also exploreGenAI DevOps, infrastructure as code, Terraform, Cloud Build, Cloud Deploy, CI/CD, model versioning, agent releases, cost optimization, caching, backup, and disaster recovery.
Throughout the course, hands-on labs help you design realistic architectures for employee assistants, customer-service copilots, document intelligence platforms, contact-center solutions, multichannel AI applications, multi-agent research systems, and automated enterprise workflows.
By the end, you will complete a capstone project that brings togetherGemini,RAG,agentic AI, enterprise data, security, governance, observability, evaluation, networking, and automation into a completeproduction-grade Google Cloud GenAI platform.
Who this course is for
Cloud architects designing enterprise Generative AI solutions.
AI engineers building Gemini, RAG, and agentic AI applications.
Solutions architects working with Google Cloud technologies.
Software developers transitioning into Generative AI engineering.
Data engineers building ingestion, retrieval, and embedding pipelines.
DevOps and platform engineers supporting production AI systems.
Security professionals responsible for AI governance and data protection.
Technical leaders planning enterprise AI modernization initiatives.
Consultants designing AI solutions for business and regulated industries.
Google Cloud professionals preparing for advanced AI architecture roles.
Students and technology professionals seeking practical enterprise GenAI skills.
Anyone interested in building secure, scalable, observable, and governed AI platforms.
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