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Course Outline
Introduction
- Overview of the Generative AI Leader certification: its value proposition and target audience.
- Detailed review of exam structure, domain weightings, and preparation strategies.
Fundamentals of Generative AI (~30%)
- Essential generative AI concepts and applications (AI, ML, LLMs, foundation models, multimodal and diffusion models, prompt engineering).
- Machine learning methodologies (supervised, unsupervised, reinforcement) and the machine learning lifecycle.
- Criteria for selecting foundation models, considering modality, context window, cost, performance, and customization options.
- Data types and quality considerations in generative AI (structured vs. unstructured, labeled vs. unlabeled).
- The stratified nature of the generative AI landscape and Google's foundation model suite (Gemini, Gemma, Imagen, Veo).
Google Cloud's Generative AI Offerings (~35%)
- Google Cloud's generative AI strengths and its AI-optimized infrastructure (TPUs, GPUs, Hypercomputer).
- Pre-built solutions: Gemini app and Gemini Advanced, Gemini for Google Workspace, and Gemini Enterprise.
- Enhancing customer experience through the Customer Engagement Suite (Conversational Agents, Agent Assist, Conversational Insights).
- Empowering developers via Vertex AI / Agent Platform, Model Garden, and RAG-enabled services.
- Generative AI agent tooling (extensions, functions, data stores) and associated Google Cloud services.
Techniques to Enhance Generative AI Model Output (~20%)
- Mitigating foundation model limitations, such as knowledge cutoffs, bias, hallucinations, and edge cases.
- Advanced prompt engineering methods (zero-shot, one-shot, few-shot, role-playing, prompt chaining, chain-of-thought, ReAct).
- Implementing grounding and Retrieval-Augmented Generation (RAG).
- Managing output via sampling parameters (temperature, top-p, token limits, and safety settings).
Business Strategies for Successful Generative AI Solutions (~15%)
- Implementation roadmap and methodologies for selecting the right solutions.
- Secure AI practices and Google's Secure AI Framework (SAIF).
- Responsible AI principles: privacy, bias and fairness, accountability, and explainability.
Exam Preparation
- Review of sample questions and a domain-by-domain analysis.
- Full mock examination with detailed answer review.
- Development of a study plan and strategy for exam day.
Summary and Next Steps
Requirements
Prerequisites
- Specific technical prerequisites are not required.
- A general understanding of business technology is beneficial.
Target Audience
- Leaders, managers, and key decision-makers.
- Business professionals in various roles integrating generative AI.
- Individuals preparing for the Google Cloud Generative AI Leader certification.
14 Hours
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