Introduction: The Hidden Cost of Infrastructure-as-a-Service

When infrastructure is self-service with no clear guardrails in place, problems can quickly emerge:

  • Teams struggle to correctly request and size GPU resources

  • Multiple teams may deploy identical models, duplicating effort

  • Expensive GPUs remain idle or become overloaded

  • Infrastructure costs escalate without clear accountability

  • Developers need model access, not GPU management complexity

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Models-as-a-Service: A Better Abstraction for AI at Scale

To address these challenges as a platform engineer, you will design a centralized, reusable, and secure model-serving layer:

Centralized Model Management

  • Platform teams deploy and maintain models with proper lifecycle management

  • Versioning, rollbacks, and testing become standardized processes

Secure API Gateway

  • All model access goes through authenticated, rate-limited endpoints

  • Full observability and monitoring across all model interactions

Developer-Friendly Access

  • Developers consume models without managing underlying hardware

  • Teams focus on building applications, agents, and features powered by AI

Efficient Resource Utilization

  • GPU resources are pooled and shared across the organization

  • Eliminates waste while ensuring fair access and cost control

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Module 1 Goals: Step into the Developer role.

In this first module, you will take your first step into the role of a developer. You will access your OpenShift web console and get access to your model credentials that have been set up through a Models-as-a-Service interface.