Google Cloud Generative AI - Fact Sheet¶

Quick Reference¶

Certification Focus: Generative AI on Google Cloud Target Audience: ML Engineers, AI Developers, Data Scientists Core Platform: Vertex AI with Gen AI capabilities Key Technologies: PaLM 2, Gemini, Codey, Imagen, Chirp Prerequisites: Understanding of ML fundamentals and GCP basics

Core Domains¶

Essential Google Cloud GenAI Services¶

Vertex AI Gen AI Studio¶

Model Garden - Pre-trained foundation models - Task-specific models (text, code, chat, embeddings) - Third-party models (Anthropic Claude, Meta Llama) - Model versioning and comparison - 📖 Model Garden Documentation - Browse and deploy models

Generative AI Studio - Prompt design and testing interface - Few-shot learning examples - Parameter tuning (temperature, top-k, top-p) - Prompt templates and management - 📖 Generative AI Studio Guide - Studio overview

PaLM API (text-bison, chat-bison) - Text generation and completion - Conversational AI with multi-turn chat - Code generation (Codey models) - Context window: 8,192 tokens - 📖 PaLM API Documentation - API reference - 📖 PaLM Best Practices - Optimization guide

Gemini Models - Gemini Pro: Advanced reasoning and understanding - Gemini Pro Vision: Multimodal (text + images) - Gemini Ultra: Most capable (when available) - Native multimodal understanding - 📖 Gemini Documentation - Gemini models - 📖 Multimodal Capabilities - Image and text processing

Embeddings and Grounding¶

Text Embeddings API (textembedding-gecko) - Generate vector representations - Semantic search and similarity - 768-dimensional vectors - Batch processing support - 📖 Embeddings API Documentation - Embedding generation - 📖 Vector Search - Similarity search at scale

Grounding with Google Search - Ground model responses with real-time search - Reduce hallucinations with factual data - Citation and source attribution - Configurable grounding sources - 📖 Grounding Documentation - Grounding setup - 📖 Vertex AI Search - Enterprise search

Retrieval Augmented Generation (RAG) - Ground responses with your own data - Vector database integration - Context injection patterns - Document retrieval and ranking - 📖 RAG Patterns - Implementation guide

Specialized Models¶

Codey (code-bison, code-gecko) - Code generation and completion - Code explanation and documentation - Multi-language support - IDE integration capabilities - 📖 Codey Models - Code generation

Imagen (imagegeneration) - Text-to-image generation - Image editing and inpainting - Style transfer and variations - Safety filtering built-in - 📖 Imagen Documentation - Image generation

Chirp (Universal Speech Model) - Speech-to-text with high accuracy - 100+ language support - Model adaptation and tuning - Streaming and batch processing - 📖 Chirp Documentation - Speech recognition

Model Tuning and Customization¶

Prompt Engineering¶

Prompt Design Principles

- Clear instructions and context
- Few-shot examples for guidance
- Output format specification
- Role and persona definition
- Constraints and boundaries

Parameter Tuning - Temperature (0-1): Randomness control (0=deterministic, 1=creative) - Top-k: Limit to top k tokens - Top-p (nucleus sampling): Cumulative probability threshold - Max output tokens: Response length control - 📖 Prompt Design Guide - Best practices

Model Tuning¶

Adapter Tuning - Efficient fine-tuning with small datasets - Preserve base model, add task-specific layers - Faster training, lower cost - Recommended for most use cases - 📖 Adapter Tuning Guide - Tuning process

Reinforcement Learning from Human Feedback (RLHF) - Improve model behavior based on preferences - Reward model training - Human-in-the-loop refinement - Advanced tuning technique - 📖 RLHF Documentation - Reward-based tuning

MLOps for Generative AI¶

Deployment and Serving¶

Vertex AI Endpoints - Deploy models to prediction endpoints - Auto-scaling based on traffic - A/B testing with traffic splitting - Monitoring and logging integration - 📖 Deploy Models - Endpoint configuration - 📖 Online Prediction - Inference APIs

Vertex AI Pipelines for GenAI - Automate model training and deployment - Data preprocessing workflows - Model evaluation and validation - Continuous training pipelines - 📖 Pipelines Guide - Workflow automation - 📖 Kubeflow Integration - Pipeline development

Monitoring and Evaluation¶

Model Monitoring - Prediction drift detection - Input/output analysis - Latency and throughput tracking - Error rate monitoring - 📖 Model Monitoring - Production monitoring

Evaluation Metrics for GenAI - ROUGE scores (summarization) - BLEU scores (translation) - Perplexity (language models) - Human evaluation frameworks - 📖 Model Evaluation - Metrics and methods

Responsible AI Practices¶

Safety and Filtering¶

Content Filtering - Toxicity detection and filtering - PII (Personally Identifiable Information) detection - Harmful content categories - Configurable threshold levels - 📖 Safety Filters - Content safety

Bias Mitigation - Fairness evaluation - Demographic parity assessment - Bias detection in outputs - Inclusive dataset curation - 📖 Responsible AI Toolkit - Fairness tools

Privacy and Governance¶

Data Privacy - Customer data isolation - No training on customer data (opt-in model tuning only) - GDPR and regulatory compliance - Data residency options - 📖 Data Governance - Regional compliance

AI Principles - Google's AI Principles - Transparency and explainability - Accountability frameworks - Ethical AI development - 📖 Google AI Principles - Ethical guidelines

Application Integration Patterns¶

API Integration¶

REST API

# Vertex AI Python SDK example
from google.cloud import aiplatform

aiplatform.init(project='your-project', location='us-central1')

model = aiplatform.TextGenerationModel.from_pretrained("text-bison")
response = model.predict(
    prompt="Write a haiku about cloud computing",
    temperature=0.8,
    max_output_tokens=256
)
print(response.text)

Common Architectures¶

Chatbot with Context - Cloud Run frontend - Vertex AI PaLM chat-bison model - Firestore for conversation history - Cloud Functions for orchestration

Document Q&A System - Document embedding generation - Vertex AI Vector Search for retrieval - PaLM for answer generation - Cloud Storage for document storage

Code Assistant - Codey models for generation - GitHub/GitLab integration - IDE plugins (VS Code, IntelliJ) - Real-time code completion

Cost Optimization¶

Pricing Models - Pay-per-use based on characters/tokens - Batch prediction for cost savings - Model selection based on task complexity - Caching for repeated queries - 📖 Pricing - Cost structure

Best Practices - Use appropriate model size for task - Implement prompt caching - Batch requests when possible - Monitor token usage - Set max output token limits

Essential gcloud Commands¶

# Enable Vertex AI API
gcloud services enable aiplatform.googleapis.com

# List available models
gcloud ai models list --region=us-central1

# Test text generation
gcloud ai endpoints predict ENDPOINT_ID \
  --region=us-central1 \
  --json-request=request.json

# Deploy custom model
gcloud ai models upload \
  --region=us-central1 \
  --display-name=my-genai-model \
  --container-image-uri=gcr.io/project/image

Key Concepts to Master¶

Model Selection¶

• PaLM 2 for text generation and chat
• Gemini Pro for advanced reasoning
• Gemini Pro Vision for multimodal tasks
• Codey for code-related tasks
• Imagen for image generation

Prompt Engineering¶

• Clear and specific instructions
• Few-shot learning examples
• System prompts and personas
• Output format specification
• Iterative prompt refinement

Production Deployment¶

• Model endpoint configuration
• Auto-scaling and load balancing
• Monitoring and alerting
• Version management
• A/B testing strategies

Responsible AI¶

• Content safety filtering
• Bias detection and mitigation
• Privacy and data governance
• Transparency and explainability
• Human oversight and review

Common Scenarios¶

Scenario 1: Customer Support Chatbot - Solution: Vertex AI chat-bison + Dialogflow CX + Firestore + Cloud Run

Scenario 2: Document Summarization Pipeline - Solution: Cloud Storage + Cloud Functions + PaLM text-bison + BigQuery

Scenario 3: Code Review Assistant - Solution: GitHub webhooks + Codey models + Cloud Functions + Slack integration

Scenario 4: Content Generation Platform - Solution: PaLM + Imagen + Cloud Run + Cloud CDN + Firebase

Scenario 5: Enterprise Search with GenAI - Solution: Vertex AI Search + PaLM + Vector Search + IAM for access control

Study Resources¶

Official Google Resources¶

📖 Generative AI Learning Path - Free Google Cloud Skills Boost path

📖 Vertex AI GenAI Documentation - Complete documentation

📖 GenAI on Google Cloud Blog - Latest updates and tutorials

📖 Google Colab Notebooks - Hands-on examples

Hands-On Practice¶

1. Basic Text Generation
2. Use Generative AI Studio
3. Test different prompts and parameters
4. Compare model outputs

5. Export code for application integration

6. Embeddings and Vector Search

7. Generate text embeddings
8. Store in Vector Search
9. Implement similarity search

10. Build RAG application

11. Model Tuning

12. Prepare training dataset
13. Tune model with adapter tuning
14. Evaluate tuned vs base model

15. Deploy to endpoint

16. Multimodal with Gemini

17. Process images with text prompts
18. Extract information from documents
19. Compare Gemini Pro vs Pro Vision

20. Build multimodal application

21. Production Deployment

22. Deploy model to endpoint
23. Implement monitoring
24. Set up A/B testing
25. Configure auto-scaling

Exam Tips¶

Focus Areas¶

• Understand model capabilities and selection criteria
• Master prompt engineering techniques
• Know RAG and grounding patterns
• Understand tuning options (adapter, RLHF)
• Be familiar with responsible AI practices
• Know deployment and monitoring strategies

Common Question Topics¶

• When to use PaLM vs Gemini vs Codey
• RAG vs fine-tuning trade-offs
• Content filtering configuration
• Token limits and context windows
• Cost optimization strategies
• Multi-model architectures

Hands-On Skills Required¶

• Use Generative AI Studio effectively
• Write effective prompts
• Implement embeddings and vector search
• Deploy models to endpoints
• Configure monitoring and alerts
• Use Python SDK for GenAI

Last Updated: 2025-01-13 Technology Focus: Vertex AI Generative AI Total Documentation Links: 50+

Notes¶

This fact sheet covers generative AI capabilities on Google Cloud Platform using Vertex AI. The GenAI landscape evolves rapidly, so always refer to the latest Google Cloud documentation for current model availability and capabilities.

Good luck with your Google Cloud GenAI journey!