Research · AI Infrastructure & Operations

MLOps & LLMOps

MLOps applies software engineering and operations discipline to machine learning. LLMOps extends it to generative AI, adding prompts, retrieval pipelines, guardrails and model providers to what must be versioned, tested and monitored.

Core practices

• Version control for code, data, models, prompts and configurations.
• Automated pipelines for training, testing and deployment.
• A model registry recording approval status and lineage.
• Controlled release with rollback.
• Monitoring in production with alerts and incident response.

What LLMOps adds

• Prompt management and testing as part of every release.
• Evaluation sets for answer quality, safety and task success.
• Management of retrieval indexes and their refresh cycles.
• Guardrails for input and output filtering.
• A gateway to route requests across model providers and control keys, quotas and costs.
• Tracing of multi-step agent workflows.

Why it matters

Without operational discipline, AI systems drift, break silently when providers update models, and become hard to audit. MLOps and LLMOps make changes safer and give governance teams the evidence they need.

Common pitfalls

• Prompts edited directly in production without testing.
• No record of which model version served which request.
• Separate toolchains for every team.

How to get started

• Put prompts and configurations under version control.
• Create evaluation sets and run them on every change.
• Add a model gateway for logging, routing and cost control.
• Standardize a small toolchain across teams.