Google Generative AI Leader: Strategy, Architecture & Enterprise Implementation
Duration
3 Day
Level
Basic to Advanced Level
Design and Tailor this course
As per your team needs
Overview
This 3-day intensive program is designed to develop Generative AI leadership capabilities using Google Cloud’s AI ecosystem. The curriculum progresses from foundational Generative AI concepts to advanced enterprise architecture, governance, and scalable deployment strategies using Vertex AI, Gemini models, and Google Cloud infrastructure.
The program equips GenAI engineers, AI/ML engineers, and data scientists with both technical depth and strategic perspective required to lead enterprise-grade GenAI initiatives — including model selection, RAG architectures, evaluation frameworks, multi-agent systems, cost governance, and Responsible AI implementation.
Audience
- Generative AI Engineers
- AI/ML Engineers
- Data Scientists
- ML Platform Engineers
- Solution Architects
- Technical Leads driving GenAI initiatives
Prerequisites
- Working knowledge of Python
- Understanding of ML fundamentals
- Familiarity with APIs and cloud concepts
- Basic Google Cloud exposure is helpful but not mandatory
Curriculum
Retrieval-Augmented Generation (RAG) Architecture
- RAG design patterns
- Embeddings generation
- Vector search in Vertex AI
- Chunking and metadata strategies
- Hybrid retrieval patterns
- RAG vs fine-tuning trade-offs
Building GenAI Applications with Vertex AI
- Vertex AI pipelines
- API orchestration patterns
- Backend integration strategies
- Stateless vs stateful design
- Memory management approaches
Agentic AI on Google Cloud
- Introduction to agentic systems
- Tool usage and function calling
- Planning and reasoning loops
- Multi-agent coordination patterns
- Observability in agent systems
Performance & Cost Optimization
- Latency reduction techniques
- Caching strategies
- Model selection for cost control
- Scaling strategies
- Quota and concurrency management
Hands-on Labs
- Build end-to-end RAG chatbot
- Integrate Vertex AI Vector Search
- Develop tool-calling agent
- Deploy scalable API endpoint
- Implement logging and monitoring
Responsible AI & Governance Framework
- Google Responsible AI principles
- Bias and fairness considerations
- Hallucination mitigation strategies
- Safety filters configuration
- Enterprise AI risk management
Security & Compliance Architecture
- Identity and Access Management (IAM)
- Service accounts and workload identity
- VPC Service Controls
- Data privacy and encryption
- Secure GenAI architecture patterns
LLM Evaluation & Monitoring
- Evaluation metrics for GenAI systems
- Human-in-the-loop validation
- Automated evaluation pipelines
- Drift detection strategies
- Continuous improvement loops
Enterprise GenAI Strategy & Roadmap
- Identifying high-impact use cases
- ROI modeling for GenAI initiatives
- Organizational readiness
- Change management strategies
- Build vs buy decisions
- AI Center of Excellence setup
Capstone Case Study (Executive-Level Simulation)
- Design enterprise GenAI architecture
- Define governance and compliance controls
- Cost and scalability planning
- Risk assessment and mitigation plan
- Present executive-ready GenAI roadmap
Upon completion, participants will be able to:
- Architect production-ready Generative AI systems on Google Cloud
- Design scalable RAG and Agentic AI solutions
- Optimize performance and manage operational costs
- Implement enterprise-grade security and governance controls
- Establish Responsible AI frameworks aligned to business objectives
- Lead GenAI transformation initiatives with strategic clarity
This program transforms technical practitioners into enterprise-ready Generative AI leaders capable of designing, governing, and scaling AI initiatives across Google Cloud environments.
Duration
3 Day
Level
Basic to Advanced Level
Design and Tailor this course
As per your team needs
