Generative AI Leader (Module 9): AI Infrastructure, Vertex AI & Enterprise Deployment Strategy

Welcome to Module 9: Strategic Framework for Generative AI Infrastructure and Deployment — where we connect infrastructure, models, and platforms into a production-ready, enterprise Gen AI architecture.

In this module, you’ll learn how Google Cloud’s AI infrastructure and Vertex AI platform enable organizations to train, deploy, and operate Generative AI at scale—while reducing operational friction and accelerating real business outcomes.

What you’ll learn in Module 9

✅ The three-layer AI deployment architecture

Infrastructure: compute, storage, and networking

Model layer: foundation models, open models, and custom training

Platform layer: orchestration, MLOps, and lifecycle management

✅ AI infrastructure fundamentals

Why traditional on-prem servers fail for Gen AI

GPUs vs TPUs: parallel processing and AI-optimized chips

Hypercomputers: large-scale GPU/TPU clusters

High-performance storage for data-hungry models

Google’s global fiber network for low-latency training

✅ The model layer on Vertex AI

Understanding the Vertex AI Model Garden (160+ models):

Gemini (Pro, Flash)

Imagen (image generation)

Veo (video generation)

Chirp 2.0 (speech)

Open models: Gemma, Llama, Mistral, Falcon, BERT

Third-party models like Claude

How to choose the right model for performance vs cost

✅ Model development approaches

AutoML: for teams with limited ML expertise

Custom training: PyTorch, TensorFlow, scikit-learn, XGBoost

When to automate vs fully customize

✅ Vertex AI as the orchestration platform

Why a unified platform matters

Fine-tuning and customization

Scalable training and deployment

Pre-trained APIs for rapid implementation

Open, framework-agnostic design (no vendor lock-in)

✅ Operationalizing AI with MLOps

Feature Store for consistency

Model Registry for version control

Model evaluation and comparison

Vertex AI Pipelines for end-to-end automation

Model monitoring for drift, skew, and performance degradation

✅ Real-world case study: Cymbal Aerospace

Automating data extraction from complex 3D CAD files

Combining AutoML + Gemini in Vertex AI Pipelines

Engineers querying CAD data using natural language

Measurable impact:

🚀 15% faster product development cycles

⏱️ 20% reduction in design review time

⚙️ Eliminated manufacturing bottlenecks

📌 Free Study Guide + Interactive Tools

Reinforce this module with exam-aligned notes, flashcards, and decision trees for the Generative AI Leader path:
👉 https://www.cloud-edify.com/google/ge...

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🎯 By the end of this module, you’ll understand how to architect Gen AI infrastructure, select the right models, and deploy AI at scale using Vertex AI and MLOps best practices.

👍 If this video helped, like, subscribe, and comment:
Which layer is hardest in your organization—infra, models, or MLOps?

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