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Prompt to Product Advanced Edition: Architecting & Scaling Multimodal Al (2025-2026)

Executive Summary

Module 1 - Architecture Overview (Gemini 2.5 3.0, Veo 3/3.1, Nano Banana)

Core Concepts

Reference Data Flow (textual diagram)

Module 2 - Internal Architecture & Data Flow

Module 3 - Performance & Cost Optimization

Token, Latency, and Cost Controls

Token Budgeting Worksheet (example)

Module 4 - Evaluation & Quality Metrics

Evaluation Log JSON (schema sketch)

Module 5 - Deployment Patterns (Vertex Al + Firebase + CI/CD)

Module 6 - Security & Governance (Audit Ready)

Module 7 - Competitive Benchmark (Q4 2025)

Module 8 - Outlook 2026

Case Study — From Prototype to Storefront in 14 Days

Appendices

Appendix A — Evaluation JSON Schema (detailed)

Appendix B — IAM Policy Snippets

Appendix C - Token & Cost Sheet (template)

MCG — Copy/Paste Section (Advanced Edition)

Learning Objectives (≤ 250 chars each)

Metadata

Quizzes

Quiz 1 — Architecture and Data Flow Fundamentals

Quiz 2 — Token Efficiency and Cost Controls

Quiz 3 — Evaluation Metrics and Logging

Quiz 4 — Deployment on Vertex AI and Firebase

Quiz 5 — Security and Governance

Quiz 6 — Competitive Benchmarking

Quiz 7 — Prompt to Product Case Study

Quiz 8 — Outlook and Migration Paths

Surveys

Survey 1 — Baseline Skills and Goals

Survey 2 — Midcourse Checkpoint

Survey 3 — Post-Course Feedback

Survey 4 — 30-Day Impact

Architecting Scalable Multimodal AI Pipelines with Gemini, Veo, Vertex is an advanced, hands-on program for builders who need to take prototypes to production. You will learn how to design token-efficient, low-latency pipelines that span text, image, audio, and video using Gemini 2.5/3.0, Veo 3/3.1, and Google AI Studio—deployed on Vertex AI with modern orchestration patterns. The course walks you through end-to-end system boundaries: ingestion, orchestration, inference, post-processing, delivery, and governance-by-design. You’ll implement event-driven data flows with Pub/Sub and Workflows, enforce least-privilege IAM, and wire automated evaluation for repeatable quality. Expect practical strategies for caching, streaming, fallback paths, cost tagging, and SLOs so you can run reliably at scale. You’ll benchmark across models for latency, quality, and cost, plan migrations through 2026, and ship an audit-ready stack with immutable logs, lineage, and human-in-the-loop gates. The capstone case study shows a 14-day path from idea to storefront with reference code paths on Vertex AI and Firebase. If you’re an AI architect, senior developer, data scientist, or creative technologist ready to productize multimodal AI, this course gives you the blueprints, templates, and playbooks to launch with confidence.

Architecting Scalable Multimodal AI Pipelines with Gemini, Veo, Vertex

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            <p>Architecting Scalable Multimodal AI Pipelines with Gemini, Veo, Vertex is a practitioner-focused program for building production-grade systems that turn prompts into shipped products. Centered on Gemini 2.5/3.0, Veo 3/3.1, and Google AI Studio, the course emphasizes architectural rigor, operational excellence, and measurable outcomes: low-latency orchestration, token-aware cost control, automated evaluation, and governance-by-design. Participants will move from primitives (text, image, audio, video) and model selection to concrete reference data flows that span API gateways, Vertex AI orchestration on Cloud Run, evaluator services, storage, and analytics—complete with streaming paths, caching layers, and resilient fallback/rollback strategies.</p>

<p>You will decompose services into prompt services, media workers, evaluators, and a policy engine, then bind them with event-driven workflows, Pub/Sub, and strict data contracts using protobuf/JSON. Performance and cost optimization are addressed with token budgeting patterns, structured outputs, retrieval gating, memoized evaluators, and per-request cost tagging with SLOs on Vertex AI. Quality is made auditable via automated metrics (e.g., CLIPScore, FID, temporal SSIM, MOS/WER), human-in-the-loop rubrics, immutable logs, and reproducible runs captured in a canonical evaluation schema. Deployment modules cover Vertex AI Endpoints, Cloud Run/Functions, Firebase front ends, CDN-backed media, and progressive release tactics (blue/green, canary, feature flags, kill switches), while security modules implement least-privilege IAM, secret rotation, data residency and encryption, and incident response playbooks ready for audit. The course closes with a competitive benchmark (Q4 2025), a 14-day prototype-to-storefront case study with reference code paths, and an outlook through 2026 that informs migration decisions, SLO targets, cost envelopes, and portfolio deprecation planning.</p>
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