llmops-platform-engineering

To set up CI/CD for LLM model deployment, you need an LLMOps platform that integrates automated evaluation gates, model promotion workflows, and rollback mechanisms. This ensures models are automatically tested for quality, safety, and latency before a safe rollout to cloud or self-hosted inference environments.

An LLMOps platform manages the lifecycle of large language models specifically, adding automated evaluation suites for quality and safety, cost control, and repeatable infrastructure for vector databases. Unlike standard MLOps, it standardizes paths for rapid LLM experimentation while maintaining strict governance and compliance.

Implement automated evaluation gates by defining standardized delivery workflows within your LLMOps platform that test model quality, safety, and latency before promotion. This prevents unsafe or underperforming models from reaching production and provides a clear rollback path if issues arise post-deployment.

Yes, platform engineering for LLMOps supports both cloud and self-hosted inference environments. By using repeatable infrastructure modules, you can maintain consistent reliability, cost control, and observability across diverse deployment targets while enabling rapid model experimentation and governance.

A production LLM platform requires repeatable infrastructure modules for inference, vector databases, and observability. These standardized components ensure reliable operation, support automated evaluation suites, and enable effective governance, rollback capabilities, and cost control across the entire model delivery workflow.