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Building Real-Time RAG Systems with Gemini & the Multimodal Live API

Chapters

1Foundations of Real-Time Retrieval-Augmented Generation

2Gemini Fundamentals: Architecture and Multimodal Capabilities

3Data Sources and Vector Stores for Real-Time RAG

4Real-Time Ingestion and Streaming Data Pipelines

5The Multimodal Live API: Authentication, Endpoints, and Workflows

6Prompt Engineering for RAG with Gemini

7Memory and Context Management in Real-Time RAG

8Latency, Throughput, and Quality of Service

9Security, Privacy, and Compliance in RAG Systems

10Evaluation, Metrics, and A/B Testing for RAG

11Deployment and Orchestration: Cloud and Edge

11.1 Kubernetes-Based Deployment Patterns 11.2 Containerization with Docker and OCI 11.3 Orchestrating Real-Time Pipelines 11.4 CI/CD for AI Services 11.5 Infrastructure as Code (Terraform, Pulumi)11.6 Edge Deployment Considerations 11.7 Hybrid Cloud Architectures 11.8 Rollouts, Canary Deployments, and Rollbacks 11.9 Telemetry-Driven Auto-Scaling 11.10 Secrets Management in Orchestration 11.11 Service Mesh and Secure Service Communication 11.12 Observability in Orchestrated Environments 11.13 Disaster Recovery for AI Services 11.14 Cost-Aware Orchestration 11.15 Data Locality and Compliance

12Case Studies: Real-World RAG Scenarios

13Advanced Retrieval Techniques: Hybrid Search and Re-ranking

14Observability: Monitoring, Logging, and Debugging

15Extending Gemini with Custom Tools and Plugins

Courses/Building Real-Time RAG Systems with Gemini & the Multimodal Live API/Deployment and Orchestration: Cloud and Edge

Deployment and Orchestration: Cloud and Edge

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Strategies for deploying RAG systems at scale, including containerization, Kubernetes, edge compute, and CI/CD pipelines.

Content

4 of 15

11.4 CI/CD for AI Services

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