Скачать или смотреть 🤖 Google Cloud Generative AI Leader | Complete Exam Review

Let's explore the fundamentals of generative AI! This quick review explains the fundamental difference between generative AI and other models, highlighting the ability of AI to generate new content. Grasp core AI concepts with these clear and concise questions and explanations.

This review highlights real-world use cases, common exam pitfalls, and essential Google Cloud services every candidate must know.

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⏱️ Timestamps
Fundamentals of Generative AI
0:11 - Definitions & Concepts
2:58 - ML & AI Lifecycle
5:13 - Foundation Models & LLMs
7:57 - Multimodal Models
10:44 - Limitations & Risks
13:16 - Prompting Basics
15:25 - Agents & Tool Use

Google Cloud’s Generative AI Offerings
17:46 - Vertex AI & Model Platform
20:25 - Gemini & Google Models
22:39 - GCP Integration
24:54 - Agent Builder & Plugins
27:07 - Search, Retrieval & RAG Tools
29:45 - Ops & Infrastructure
31:54 - APIs & Prebuilt Services

Techniques to Improve Model Output
34:18 - Advanced Prompt Engineering
36:58 - Fact-Checking
39:22 - Adaptation
41:45 - Human-in-the-Loop (HITL)
44:08 - Bias
46:42 - Evaluation & Metrics

Business Strategies for AI Solutions
49:06 - Use Case Identification
51:34 - Change Management & Adoption
53:47 - Governance, Ethics, & Responsible AI
55:26 - Security, Privacy, Compliance
57:15 - ROI & KPIs
58:59 - Scaling & Cost Strategy

📚 Topics Covered
Generative AI Fundamentals: What generative AI is, its characteristics, and examples like GANs (0:06).
AI Lifecycle: Phases of AI development, including data collection, training, and evaluation (2:58).
Foundation Models & LLMs: Key features and advantages of large language models and foundation models (5:12).
Multimodal Models: How models integrate different data types for richer outputs (7:57).
Limitations & Risks: Challenges such as data privacy, bias, and potential for inaccurate outputs (10:44).
Prompting Basics: The importance of clear and effective prompts for guiding AI output (13:15).
Agents & Tool Use: The function of AI agents and how they use tools to interact with external systems (15:25).
Google Cloud Offerings: An overview of Google Cloud services for generative AI, including Vertex AI, Gemini, AI Studio, Agent Builder, and RAG tools (17:42).
Improving Model Output: Techniques like advanced prompt engineering, grounding, fine-tuning, and human-in-the-loop (34:14).
Business Strategies: Identifying use cases, change management, governance, ethics, security, ROI, and scaling for AI solutions (49:01).

🔗 Helpful Links
Birdsy AI/ML/Cloud Exam Prep: http://birdsy.ai
Google Cloud Certification Overview: https://cloud.google.com/certification
Vertex AI Documentation: https://cloud.google.com/vertex-ai/docs

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