Follow the MLOps for LLM Systems learning path on AI Builders Network - a free, structured roadmap with curated resources and step-by-step progress tracking.
This path is for platform, DevOps, and SRE engineers asked to put LLM systems on production footing. Your existing discipline transfers, CI/CD, observability, incident response, but the surface changes: prompts are config, quality is probabilistic, and your most critical dependency is an external API you don't control.
By the end you'll be able to: Version and review prompts and model configs like production code; Gate deploys on behavioral eval suites, including model upgrades; Run an inference gateway with routing, caching, and failover; Attribute LLM cost per feature and alert on quality drift; Write SLOs and incident runbooks that cover answer quality.
Classical MLOps centers on training pipelines, feature stores, and model registries. LLMOps shifts the weight to what changes with API-based models: prompt and config versioning, behavioral evaluation gates, token cost management, and reliability engineering around external provider dependencies. Serving infrastructure overlaps; the artifacts under management differ.
Treat behavior like tests: every prompt, model, or retrieval change triggers an eval suite, and scores below threshold block the merge exactly like failing unit tests. Model version upgrades go through canary deploys: a provider's new model version is a production change even though you deployed no code.
Instrument first: per-feature and per-customer token attribution usually reveals that a handful of call sites drive most spend. Then apply the levers in order of effort: prompt caching, response caching, output length limits, routing easy requests to smaller models, and batching offline work. Teams routinely cut 40-70% without quality loss.