Google Vertex AI Generative AI Development Training Course

Course overview

Organisations are moving from generative AI experiments to applications that must be secure, observable, cost-controlled and useful to real teams. Developers and technical leads need more than prompt-writing ability: they must select suitable Gemini models, ground responses in enterprise data, evaluate quality, manage safety controls and deploy solutions through Google Cloud without exposing sensitive information. This five-day Google Vertex AI Generative AI Development Training Course addresses those implementation decisions through practical work on production-oriented generative AI patterns.

Participants build and assess generative AI solutions using Vertex AI, Gemini models, Vertex AI Studio, Model Garden, vector search and evaluation tooling. The course covers prompt design, structured output, function calling, retrieval-augmented generation (RAG), embeddings, document chunking, safety settings, responsible AI controls, identity and access management, monitoring and cost estimation. Participants learn how to turn a business requirement into an architecture, prototype, evaluation plan and deployment approach that can be reviewed by engineering, security and product stakeholders.

Instruction combines guided demonstrations with hands-on labs in a Google Cloud environment, technical design reviews and scenario-based workshops. Each day develops part of a capstone solution, from use-case selection through RAG implementation and operational controls. Participants leave with a documented Vertex AI generative AI solution blueprint, including prompt assets, evaluation criteria, architecture decisions, security controls, cost assumptions and an implementation backlog that can be adapted for a workplace initiative.

The course is designed for professionals who already work with cloud applications, data platforms or machine learning services and now need to deliver governed generative AI capabilities on Google Cloud.

Course objectives

By the end of this course, participants will be able to:

• Design a Vertex AI generative AI architecture for a defined business use case
• Configure Gemini prompts with system instructions, few-shot examples and structured JSON output
• Build a retrieval-augmented generation pipeline using embeddings, chunking and vector search
• Implement function calling patterns that connect Gemini applications to approved enterprise services
• Evaluate model responses using test datasets, scoring criteria and Vertex AI evaluation workflows
• Apply safety settings, content filtering and responsible AI controls to generative AI interactions
• Estimate token, model and retrieval costs for a Vertex AI application workload
• Produce a deployable solution blueprint with IAM, monitoring, evaluation and rollout requirements

Benefits of attending

Target competencies

Who should attend

• Cloud Application Developers — who need to build Gemini-enabled services on Google Cloud
• Machine Learning Engineers — who must operationalise foundation-model applications with evaluation and controls
• Data Engineers — who prepare enterprise content and retrieval pipelines for RAG solutions
• Solutions Architects — who define secure, scalable Vertex AI reference architectures
• Technical Product Managers — who need to specify feasible generative AI features and acceptance measures
• Platform and DevOps Engineers — who support identity, deployment, observability and cost governance for AI workloads

Requirements and prerequisites

Participants should be comfortable writing or reviewing basic Python, making REST API calls, and navigating a Google Cloud project. Familiarity with core Google Cloud concepts, including projects, service accounts, IAM roles, Cloud Storage and billing, is assumed. Participants should understand JSON, HTTP requests and the basic distinction between training, inference and evaluation. Prior machine learning model-building experience is helpful but not essential; the course focuses on using and engineering with foundation models rather than training models from scratch. No prior Vertex AI, Gemini API, vector database or prompt-engineering certification is required.

Training methodology

The instructor uses short technical briefings followed by guided work in Vertex AI Studio and Google Cloud. Participants configure Gemini prompts, inspect model behaviour, build retrieval flows, compare evaluation results and troubleshoot common failure modes in structured lab exercises. A running enterprise knowledge-assistant case provides the context for group architecture reviews, where teams justify data boundaries, model choices, safety settings and operating costs. Each participant completes an end-of-course application plan that translates the capstone into a scoped workplace implementation.

Course outline

Day 1: Vertex AI foundations and generative AI solution design

• Vertex AI platform components and generative AI workflow
• Gemini model families, capabilities and model selection criteria
• Google Cloud project structure, APIs, quotas and billing controls
• Vertex AI Studio prompt development workspace
• System instructions, role prompting and few-shot prompting patterns
• Multimodal inputs and structured JSON response schemas
• Use-case discovery, success measures and generative AI architecture decisions

Workshop: Participants prototype a Gemini-based internal support assistant in Vertex AI Studio and produce a use-case canvas with measurable acceptance criteria.

Day 2: Prompt engineering, tools and application integration

• Prompt templates, variables and reusable prompt assets
• Temperature, token limits and response consistency trade-offs
• Schema-constrained generation and JSON validation patterns
• Function calling and tool declaration design
• Grounding versus tool use for enterprise application scenarios
• Gemini API requests, authentication and error handling
• Python client implementation patterns for Vertex AI applications

Workshop: Participants build a Python prompt workflow that returns validated structured responses and invokes a defined business lookup function.

Day 3: Retrieval-augmented generation with enterprise content

• RAG architecture and retrieval failure modes
• Embedding models and semantic similarity search
• Document ingestion, cleaning and metadata enrichment
• Chunking strategies, overlap settings and citation design
• Vertex AI Vector Search index configuration
• Retrieval ranking, context assembly and grounded generation
• BigQuery and Cloud Storage content patterns for RAG pipelines

Workshop: Participants create a small RAG prototype from policy documents, test retrieval quality and produce cited answers to a business question set.

Day 4: Evaluation, responsible AI and operational governance

• Generative AI evaluation datasets and representative test cases
• Point-based metrics, rubric evaluation and pairwise comparison
• Vertex AI evaluation workflows and experiment tracking
• Hallucination, relevance, groundedness and instruction-following measures
• Safety settings, harmful-content categories and response controls
• Prompt injection, data leakage and adversarial input mitigations
• IAM roles, audit logging and least-privilege access for AI services

Workshop: Participants evaluate their RAG prototype against a labelled test set and produce a risk register with prioritised remediation actions.

Day 5: Deployment planning, observability and production readiness

• Reference architectures for Vertex AI generative AI applications
• Endpoint selection, scalability and latency planning
• Token accounting, quotas and cost-estimation methods
• Prompt versioning, release controls and rollback strategies
• Application logging, tracing and model response observability
• Human review, feedback capture and continuous evaluation loops
• Implementation roadmaps, governance gates and stakeholder sign-off

Workshop: Participants complete and present a production-ready Vertex AI solution blueprint containing architecture, evaluation plan, controls, cost assumptions and a 90-day implementation backlog.

Tools & standards covered

Google Vertex AI, Vertex AI Studio, Vertex AI Vector Search, BigQuery

A typical training day

Live online deliveries follow the same structure in the East Africa Time zone, with shorter screen blocks and longer breaks.

What the fee includes

How you can take this course

Certification

Participants who complete the full five days receive the Skillset Development Certificate of Completion, stating the course title, course code, dates and delivery format — suitable for professional-development records and employer reimbursement.

Frequently asked questions