Generative AI Leader
In this course, you will take a journey from a broad overview of gen AI to understanding how to leverage gen AI and Google Cloud for organizational transformation.
What you will learn
- Describe how generative AI transforms organizations across business functions and industries.
- Define core gen AI concepts (artificial intelligence, gen AI, machine learning, etc.).
- Identify the core layers of the gen AI landscape (applications, agents, platforms, models, and infrastructure).
- Explain how you can combine the components of generative AI agents to build powerful solutions.
- Identify Google Cloud products and solutions that support the implementation and scaling of generative AI initiatives.
Prerequisites
- None
Target audience
- Business professionals of all levels and job roles who want to contribute to their company's gen AI transformation
Training Program
5 modules to master the fundamentals
Objectives
- Discover how generative AI transforms organizations across business functions and industries.
- Explore how generative AI uses foundation models and prompt engineering to create value.
- Identify Google Cloud's unique strengths in the field of generative AI.
- Explain the Google-Cloud-recommended steps to successfully implement a transformational gen AI solution.
Topics covered
- →Introduction to gen AI for businesses
- →Introduction to gen AI foundations
- →Gen AI strategy
Activities
Discussion: What is your why?
Discussion: Feeling inspired
Try it: Hands-on with the Gemini app
Discussion: Benefits of Google Cloud when building AI solutions
Try it: Prioritization exercise
Activity: Augmentation or automation?
Objectives
- Define core generative AI concepts.
- Explain how data types are used in generative AI for business solutions, innovation, and competitive edge.
- Explain the role of foundation models in generative AI.
- Describe the Google-Cloud-recommended strategies for handling LLM limitations.
- Describe the challenges and key factors involved in responsibly and securely developing and deploying AI.
Topics covered
- →Core gen AI concepts
- →Foundation models
- →Responsible AI
Activities
Activity: Supervised, unsupervised, or reinforcement learning?
Discussion: Model matchmaker
Discussion: Organizational roadblocks to responsible AI
Objectives
- Describe the layers of the gen AI landscape.
- Determine strategic entry points within the gen AI landscape to address specific business needs and drive innovation.
- Describe the components of the Google Cloud gen AI portfolio.
- Explain how Google Cloud's AI-optimized resources support gen AI development.
- Describe the business factors to consider when deciding what to use for specific applications.
Topics covered
- →The gen AI landscape
- →Gen AI agents and applications
- →Gen AI platform, model, and infrastructure
- →Gen AI project resources and management
Activities
Discussion: Conversational and workflow agents
Activity: The writing assistant
Activity: Edge or cloud?
Activity: Best solution
Objectives
- Explain prompt engineering techniques and how they drive better results.
- Develop automated workflows by using generative AI prompt templates and tools.
- Describe the core functionality and business value of using Gemini for Google Workspace.
- Describe the core functionality and business value of using Gemini for Google Cloud.
Topics covered
- →Prompting techniques
- →Gen AI for productivity
- →Gemini for Google Cloud
Activities
Discussion: Customer service
Try it: Step into the role
Activity: Pick-a-product
Discussion: Gemini for Google Cloud in your organization
Objectives
- Define the components of generative AI agents and how they work together.
- Explain how you can combine the components of generative AI agents to build powerful solutions.
- Describe how you can use Google Cloud's gen AI products to create generative AI agents and solutions.
- Explain how to effectively lead and manage organizational transformation with gen AI.
Topics covered
- →Today's agents
- →Building agents
- →Enhancing the customer experience with agents
Activities
Activity: The better prompt technique
Activity: Agent tooling example – Meeting location planner APIs
Activity: Pick-a-product
Quality Process
SFEIR Institute's commitment: an excellence approach to ensure the quality and success of all our training programs. Learn more about our quality approach
- Lectures / Theoretical Slides — Presentation of concepts using visual aids (PowerPoint, PDF).
- Technical Demonstration (Demos) — The instructor performs a task or procedure while students observe.
- Guided Labs — Guided practical exercises on software, hardware, or technical environments.
- Quiz / MCQ — Quick knowledge check (paper-based or digital via tools like Kahoot/Klaxoon).
The achievement of training objectives is evaluated at multiple levels to ensure quality:
- Continuous Knowledge Assessment : Verification of knowledge throughout the training via participatory methods (quizzes, practical exercises, case studies) under instructor supervision.
- Progress Measurement : Comparative self-assessment system including an initial diagnostic to determine the starting level, followed by a final evaluation to validate skills development.
- Quality Evaluation : End-of-session satisfaction questionnaire to measure the relevance and effectiveness of the training as perceived by participants.
Train multiple employees
- Volume discounts (multiple seats)
- Private or custom session
- On-site or remote