MLOps for LLMs
Apply MLOps practices to LLMs: versioning, CI/CD, monitoring, incident response, and lifecycle management for production AI.
TL;DR
MLOps for LLMs includes: prompt versioning, evaluation in CI/CD, production monitoring, incident response, and managing the full model lifecycle from development to retirement.
Versioning
What to version:
- System prompts
- Model versions
- Retrieval configurations
- Evaluation datasets
Tools: Git for prompts, model registries for models
CI/CD pipeline
- Commit prompt change
- Run automated evaluations
- Compare to baseline
- If passing, deploy to staging
- Canary deployment to production
- Monitor and rollback if needed
Monitoring
System metrics: Latency, error rate, throughput
Quality metrics: LLM-as-judge scores, user feedback
Cost metrics: Token usage, API spend
Business metrics: User engagement, task completion
Incident response
- Automated alerts on degradation
- Runbooks for common issues
- Rollback procedures
- Post-incident reviews
Evaluation
- Regression test suite
- A/B testing framework
- Shadow deployments
- Human evaluation sampling
Tools
- LangSmith: Tracing, evaluation, monitoring
- Weights & Biases: Experiment tracking
- MLflow: Model registry, deployment
- Datadog/Grafana: Monitoring
Best practices
- Automate everything
- Monitor proactively
- Document thoroughly
- Practice rollbacks
- Blameless postmortems
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Key Terms Used in This Guide
AI (Artificial Intelligence)
Making machines perform tasks that typically require human intelligenceâlike understanding language, recognizing patterns, or making decisions.
LLM (Large Language Model)
AI trained on massive amounts of text to understand and generate human-like language. Powers chatbots, writing tools, and more.
Machine Learning (ML)
A way to train computers to learn from examples and data, instead of programming every rule manually.
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