LLMOps in Practice- Building Secure, Governed Pipelines 4 LLMs
summary
Claims a reproducible three‑tier LLMOps pipeline (sandbox → runner → production) with a working demo app that shows RBAC, strict prompt templates, logging and audit trails, and a prompt regression suite for tests. It reports using MLflow, Ray, ELK and TensorFlow Data Validation for versioning, monitoring and observability, measures semantic drift with a Jaccard score over about a week, and shows the defenses (hallucination detector, prompt‑injection guards) integrated and exercised end to end.
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chapters (14)
- 0:00LLMOps introduction
- 1:09LLMOps vs MLOps differences
- 2:34LLM production failure modes
- 4:59MLOps vs LLMOps pipelines
- 7:28Three-tier LLM ops architecture
- 8:10Sandbox for LLM experiments
- 11:41Runner with safety checks
- 14:48Production with RBAC and observability
- 17:36Observability importance
- 20:01Code walkthrough three-tier setup
- 23:09Demo RBAC role interactions
- 28:12LLM deployment best practices
- 30:21LLMOps tools and resources
- 31:07Q&A
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