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AI for Platform Engineering

AI systems are production systems. Treat them with the same rigor you apply to apps and services. This page outlines how AI (including LLMs) fits into platform engineering and DevOps workflows.

Scope​

  • Model lifecycle: data → training → evaluation → deployment → monitoring → retraining
  • Infrastructure: GPUs/accelerators, autoscaling, storage, artifact registries
  • Reliability: SLOs for ML endpoints, rollback strategies, canaries/blue‑green
  • Security: data governance, model supply‑chain, prompt injection mitigation for LLMs

Core Principles​

  • Automate everything: CI/CD for models, datasets, and inference services
  • Version everything: datasets, features, models, prompts, configs
  • Observe everything: latency, throughput, accuracy, drift, safety incidents
  • Contain cost: batching, caching, quantization, right‑sizing hardware

Reference Architecture​

  • Feature store and dataset lineage
  • Model registry with immutable artifacts and metadata
  • Training pipelines (on-prem/cloud) with reproducibility and checks
  • Inference layer (REST/gRPC) behind an API gateway with auth/rate limits
  • Monitoring (metrics/logs/traces) + evaluation pipelines and alerting

When to Use LLMs vs. Traditional ML​

  • Use LLMs for generative tasks, semantic search, and complex reasoning
  • Prefer traditional ML for tabular predictions and deterministic pipelines
  • Consider hybrid patterns: retrieval‑augmented generation (RAG) with domain data