Three-Layer MLOps Architecture

How Configuration Separation Cuts Infrastructure Costs by 99% While Improving Team Productivity

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Configuration Layer

Purpose: Store instructions, not installations

Technology: GitHub Repository

Contents: requirements.txt, .env templates, documentation

Size: 50KB (vs 5GB traditional)

Benefit: Permanent, shareable, version-controlled recipes

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Execution Layer

Purpose: Runtime development environment

Technology: GitHub Codespace

Contents: Running Python, 289 packages, VS Code interface

Duration: Temporary, rebuilds from Layer 1

Benefit: Zero local setup, browser-accessible, consistent

☁️

Infrastructure Layer

Purpose: AI services and cloud resources

Technology: Azure OpenAI & Cloud Services

Contents: GPT models, storage, compute resources

Scaling: Pay-per-use, scales to zero when idle

Benefit: Enterprise features without enterprise costs

Proven Results: Real Implementation Data

99%

Storage Reduction
50KB vs 5GB repositories

95%

Faster Setup
15 minutes vs 6 hours

100%

Environment Consistency
Zero "works on my machine"

$15

Monthly Total Cost
Complete learning program

Applied MLOps: Reusable Patterns for Real-World ML Systems

This architecture scales seamlessly from individual learning projects to enterprise production environments. Professional capabilities without professional budgets. Enterprise patterns without enterprise complexity.

The Traditional MLOps Problem

Configuration Layer

Execution Layer

Infrastructure Layer

Proven Results: Real Implementation Data

Applied MLOps: Reusable Patterns for Real-World ML Systems