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Advanced Multimodal AI Field Guide 2025

Executive Summary

Part I — Veo 3.1 Field Guide

Core Capabilities (Oct 2025)

Latency & Quality Benchmarks

JSON Contract (excerpt)

Part II — Haiku 4.5 Cost Track

When to Switch to Haiku 4.5

Cost & Speed Snapshot (illustrative)

Alerting & Budget Dashboard (BigQuery)

Best Practices

Capstone Exercise

MCG — Copy/Paste Section (EN)

What is your course about? (≤ 250 chars)

Teaching goals (≤ 250 chars each)

Audience / Tone / Quiz / Survey / Images

Architecting & Scaling Multimodal AI Pipelines in Google Cloud (EN)

Reference Architecture

Orchestration & Operations

Evaluation & Observability

Governance & Compliance

BigQuery Schemas (snippets)

Cost Strategy (with Haiku 4.5 as low-cost lane)

Capstone Checklist

MCG - Copy/Paste Section (EN)

Perplexity Comet Pro — Mastering AI Driven Browsing & Knowledge Automation (Oct 2025)

Getting Started

Multisource Research & Bias Analysis

Automation & Organization

Professional Use Cases

Integrations (Gemini / Claude / ChatGPT)

Evaluation & Governance

Capstone Project

Hybrid BI Mastery: Microsoft Fabric, Power BI, BigQuery, Looker

Learning Objectives

Module 2 – Connecting Power BI to BigQuery (Secure & Performant)

Performance & Cost Tips

Module 4 – Governance & Security in Multi Cloud Bl

Module 6 – Hands On Project: Hybrid Executive Dashboard

Quiz and Survey

Quiz 1

Quiz 2

Quiz 3

Quiz 4

Quiz 5

Quiz 6

Quiz 7

Quiz 8

Survey 1

Survey 2

Survey 3

Survey 4

Build end-to-end, cross-cloud multimodal AI and analytics systems that your executives can trust and your engineers can run. Cross‑Cloud Multimodal AI Architecture Mastery blends Google Cloud’s AI/BigQuery stack with Microsoft Fabric and Power BI to deliver low-latency insights, governed cost control, and production reliability. You’ll learn when to route workloads to premium models (e.g., Veo 3.1) versus low-cost lanes (e.g., Haiku 4.5), how to wire observability and budget guardrails in BigQuery, and how to operationalize AI-driven browsing and knowledge automation with Perplexity Comet Pro. Then, you’ll bring it all together in a hybrid BI pattern: Looker/BigQuery for governed metrics and Power BI/Fabric for executive-ready dashboards—without duplicating data or breaking security. Through field guides, reference architectures, and a capstone, you’ll configure JSON contracts, tune latency vs. quality tradeoffs, enforce row-level security across platforms, and stand up cost-aware orchestration. This course is designed for real-world delivery: clear decision trees, benchmark-informed choices, and copy/paste-ready templates to accelerate your next production deployment.

Cross‑Cloud Multimodal AI Architecture Mastery: Fabric, Power BI, Google Cloud

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            <p>Cross‑Cloud Multimodal AI Architecture Mastery: Fabric, Power BI, Google Cloud is designed to equip technical leaders and senior practitioners with the patterns, benchmarks, and operational discipline required to build reliable, low‑latency multimodal systems across Microsoft and Google stacks. The course integrates an advanced field guide to the 2025 multimodal landscape (Veo 3.1 and Haiku 4.5), a cost‑aware orchestration playbook for Google Cloud pipelines, and a practical Hybrid BI track that standardizes executive reporting across Power BI, Fabric, BigQuery, and Looker. You will learn when to engage premium inference lanes for quality, when to pivot to cost lanes for scale, and how to enforce governance across data planes and semantic layers without sacrificing developer velocity or end‑user experience.</p>

<p>Part I provides a Veo 3.1 field guide with core capabilities, latency/quality trade‑offs, and a JSON contract excerpt to formalize model interfaces. Part II establishes a Haiku 4.5 cost track—detailing switch criteria, cost/speed snapshots, BigQuery‑based alerting and budget dashboards, and operational best practices that keep spend predictable under load. The Google Cloud architecture section translates these choices into deployable patterns: reference architectures, orchestration and operations, evaluation/observability, governance/compliance, and BigQuery schema snippets that accelerate implementation. In parallel, the Hybrid BI modules show how to connect Power BI securely and performantly to BigQuery, optimize DirectQuery/Import choices, align Fabric with Looker for governed self‑service, and culminate in a hands‑on hybrid executive dashboard. Throughout, embedded quizzes, a capstone checklist, and a final exercise ensure you exit with battle‑tested designs, measurable performance baselines, and a repeatable methodology for cross‑cloud multimodal AI and BI integration.</p>
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