This course contains the use of artificial intelligence.

Build the skills required to design, secure, deploy, and operate production-ready enterprise generative AI systems on Google Cloud. This comprehensive, hands-on course takes you beyond basic AI demonstrations and teaches you how to architect reliable RAG applications, intelligent AI agents, multimodal experiences, and governed enterprise AI platforms using Gemini, Google Cloud, and modern cloud architecture practices.

You will begin by exploring the complete Google 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 of Gemini models, Model Garden, Gemini Enterprise Agent Platform, the Agent 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 is Retrieval-Augmented Generation on Google Cloud. You will learn how to ingest enterprise documents, process files with Document 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 compare Vector Search, Agent Retrieval, AlloyDB, BigQuery, Cloud SQL, Spanner, and Firestore for enterprise retrieval workloads.

You will also design scalable application platforms using Cloud Run, Google Kubernetes Engine, Compute Engine, Cloud Functions, Eventarc, Pub/Sub, Dataflow, Workflows, and Application Integration. The course explains how services such as Cloud 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 implement Model 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 use Cloud Monitoring, Cloud Logging, Cloud Trace, distributed tracing, evaluation datasets, retrieval metrics, groundedness measurements, safety evaluations, regression testing, and release quality gates. You will also explore GenAI 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 together Gemini, RAG, agentic AI, enterprise data, security, governance, observability, evaluation, networking, and automation into a complete production-grade Google Cloud GenAI platform.