کتاب هوش مصنوعی مولد در گوگل کلاود

چالش توسعه برنامه‌های هوش مصنوعی مولد

Overview of LLMs, Generative AI Agents, and Potential Applications to Business Tasks • Small Language Models (SLMs) • Foundation Models and Multimodality • Domain-Specific and Reasoning Models • Generative AI Agents • Agent Architectures • Challenges in Development, Deployment, and Maintenance • Development Challenges • Deployment Challenges • Maintenance Challenges • Addressing Challenges with Modern Platforms • Industry Use Cases and ROI • Looking Ahead • Learning Labs

آمادگی و دسترسی‌پذیری داده

The Amplified Importance of Data for GenAI • What Data Readiness Really Means for GenAI Applications • Key Dimensions of Data Readiness • The Interconnected Nature of Data Readiness • Managing Prompts as Data Assets • The Human Element: Roles in the Data Readiness Journey • Data Scientists: The Explorers • ML Engineers: Building the Bridge to Production • Data Engineers: Architecting the Foundation • DevOps and SREs: Operationalizing the Foundation • Business SMEs and Domain Leaders: The “Why” Behind the “What” • Strategic Data Patterns: The Foundation for Reliable GenAI Systems • The Unified Data and AI Platform • From RAG to Agentic RAG: The Evolution of a Data Pattern • Tying it All Together: the Enterprise RAG Knowledge Engine • Data Readiness for Agent Systems • Security and Governance: Protecting Data Throughout the LLM Lifecycle • Data Privacy Framework • Comprehensive Governance • Practical Data Readiness Assessment • Looking Ahead • Learning Labs

ساخت عامل چندوجهی با کیت توسعه عامل

From Zero to Agent in Seven Lines • The Simplest Thing That Works • The Runtime Behind the Simplicity • Running Your First Conversation • Understanding the Limitations • Adding Intelligence Through Tools • Your Agent’s First Tool • Tools Versus Subagents–A Practical Decision Framework • State Management That Actually Scales • Building a Stateful Shopping Cart • Understanding the Three Scopes • State Scope Interactions • Making State Persist in Production • Beyond Structured State: Semantic Memory • Vertex AI Agent Engine Memory Bank: Learning from Conversations • Implementation • Expanding to Multimodal • Making Our Agent See • From Static Analysis to Live Support • Building Complete Interaction Memory • Building Production-Grade Tools • Handling Asynchronous Operations • Ensuring Safety with Human-in-the-Loop • Production Monitoring and Policy Enforcement with Callbacks and Plug-ins • Looking Ahead • Learning Labs

هماهنگ‌سازی تیم‌های عامل هوشمند

The Bottleneck of the Monolithic Agent • Conflicting Instructions • Tool Selection Paralysis • Token Limitations • Maintenance Nightmare • The Solution: An Agent Team • The Roadmap: From Local Teams to Distributed Systems • Local Teams • The Foundation: Agent Hierarchy • Pattern 1: The Assembly Line (SequentialAgent) • Pattern 2: The Independent Taskforce (ParallelAgent) • Pattern 3: The Iterative Refiner (LoopAgent) • Distributed Collaboration • The Organizational “Why” • MCP: The Language of Tools • A2A: The Language of Delegation • Putting It All Together: A Hybrid Agent Team • Production Realities • The Trust Problem: Security Schemes in A2A • The Extension Problem: Evolving Agent Capabilities • The Visibility Problem: Distributed Tracing • The Versioning Problem: Managing Agent Evolution • Looking Ahead • Edge and Embodied Intelligence • From Architecture to Excellence • Learning Labs

استراتژی‌های ارزیابی و بهینه‌سازی

Tailoring Evaluation to Your LLM/Agent’s Purpose • Beyond Basic Functionality • Key Dimensions of Evaluation • Setting the Bar for Production Excellence • Practical Evaluation Strategies • Human-Centered Evaluation • A/B Testing and Preference Scoring • Red Teaming: Stress Testing for Safety and Reliability • Automated Evaluation: Scaling Feedback for Rapid Improvement • Reference-Based Metrics for Text Generation • Limitations of Reference-Based Evaluation • Domain-Specific and Task-Oriented Metrics • Metrics for Agentic Systems and Tool Use • Optimization Strategies • Refining Prompts • Elevating Agent Performance • Beyond Prompt and Agent Optimizations • Looking Ahead • Learning Labs

تنظیم دقیق و زیرساخت

The Tuning Decision • The Fine-Tuning Decision Framework • Fine-Tuning Strategies: From Full Training to Efficient Adaptations • The Real Cost of Fine-Tuning • Implementation Approaches • Infrastructure Questions Emerge • The Constraint You’ll Hit First • Pattern 1: The Waiting Accelerator • Pattern 2: The Memory Wall • Pattern 3: Maxed Out But Still Slow • Pattern 4: More GPUs = Worse Performance • Accelerators: Matching Hardware to Bottlenecks • The Decision Framework • The Practical Decision • Migration Reality • Storage Options • When Storage Becomes Your Bottleneck • The Storage Pattern • Serving and Deployment • Configuration That Matters • Connecting Models to Agents • Agent Deployment Platforms • Agent Engine • Cloud Run • GKE • Looking Ahead • Learning Labs

عملیات یادگیری ماشین برای سیستم‌های هوش مصنوعی آماده تولید

From Ad Hoc to Systematic: The Current State of Teams • The Evolution of MLOps • Building Reproducible Training Pipelines • Data Versioning and Lineage • Experiment Tracking • Model Registry and Governance • Automated Retraining • Comprehensive Monitoring • Agent Monitoring • Technical Monitoring • Hallucination Detection • CI/CD for AI Systems • Cloud Build • Cloud Deploy • Security and Governance as Foundation • Security Framework for AI Agents • Model Armor: A Key Security Component • Cost Management • The True Cost Model • Cost Attribution Strategies • Intelligent Cost Operations • Spending Controls • Looking Ahead • Learning Labs

چارچوب بلوغ هوش مصنوعی و عامل‌محور

What Is the AI and Agentic Maturity Framework? • The Maturity Dimensions and Phases • Vision and Leadership (The “What” and the “Why” Dimension) • Talent and Culture (The “Who” Dimension) • Operational and Technical Practice (The “How” Dimension) • How the Three Dimensions of AI and Agentic Maturity Can Work Together • From Framework to Reality: What Are Teams Actually Building, and How? • Technical Conversations • Leadership, Talent, and Culture Conversations • Why and How a Platform Approach Can Accelerate an Organization’s AI and Agentic Maturity • Vertex AI Platform • Learning Labs