Platform Engineering Fundamentals: Building AI-Ready Infrastructure with IoT and Identity Integration

This module establishes what platform engineering is, how it evolved from DevOps and SRE practices, and why organizations are investing in platform teams. You'll learn the core principles of treating platforms as products, the difference between platforms and tooling, and how cognitive load theory informs platform design. We'll examine the sociotechnical nature of platforms and introduce the concept of 'golden paths' that guide without restricting developers.

This module covers the architectural layers of modern platforms—from infrastructure automation to developer portals—and surveys the technology ecosystem. You'll explore platform capabilities like environment provisioning, CI/CD orchestration, observability, and secrets management. Special attention is given to how platforms must evolve to support AI/ML workloads with GPU scheduling, model registries, and experiment tracking.

Before building platforms that abstract infrastructure, you need to understand infrastructure automation itself. This module covers Infrastructure as Code (IaC) principles, declarative versus imperative approaches, and state management. You'll learn Terraform fundamentals, GitOps workflows, and how platforms use IaC engines as building blocks. The goal is fluency with automation tools that platform capabilities are built upon.

Identity platforms provide authentication, authorization, and user management as a service to multiple applications—a perfect platform engineering example. This module explores how to design identity platforms using protocols like OAuth 2.0, OpenID Connect, and SAML. You'll learn multi-tenancy patterns, API design for identity operations, and how to build developer-friendly authentication experiences. AI considerations include securing model APIs and implementing fine-grained authorization for AI services.

IoT platforms manage device lifecycle, connectivity, data ingestion, and security for thousands to millions of devices. This module examines IoT platform architecture including device provisioning, certificate management, message routing, and fleet updates. You'll learn how platforms abstract device heterogeneity, provide self-service device onboarding, and integrate with AI services for edge intelligence and anomaly detection. This demonstrates platform engineering at massive scale.

AI workloads require specialized platform capabilities beyond traditional application hosting. This module covers MLOps platform components including experiment tracking, model registry, feature stores, and model serving infrastructure. You'll learn how platforms provide self-service ML workflows, manage GPU resources, enable A/B testing of models, and monitor model performance. We'll also explore how generative AI tools are changing platform development itself through infrastructure code generation and automated documentation.

The best platform capabilities are useless if developers can't easily discover and use them. This module focuses on developer portals, service catalogs, documentation strategies, and self-service workflows. You'll learn how to design platform interfaces using tools like Backstage, implement software templates for golden paths, and measure developer satisfaction. We'll cover how to balance flexibility with guardrails, and create feedback loops that continuously improve the platform.

Platform engineering is as much about people and processes as technology. This module addresses how to structure platform teams, define their charter, and establish healthy relationships with application teams. You'll learn platform governance models, contribution processes, and how to make platforms evolutive through SRE practices. We cover financial aspects like showback/chargeback, security integration, and creating a platform roadmap that aligns with business needs while managing technical debt.