References: LLMOps

Source material

Source curriculum (structural mirror, cited as further study):
• Full Stack Deep Learning, "LLM Bootcamp" (Spring 2023):
    LLMOps
  Instructors: Charles Frye, Sergey Karayev, and Josh Tobin
  Course page: https://fullstackdeeplearning.com/llm-bootcamp/
  Lecture videos: publicly available on the Full Stack Deep Learning
    YouTube channel
  License: bootcamp materials are published free to view but no explicit
    license (Creative Commons or otherwise) is published; lecture videos
    are on YouTube under standard terms.
  Required attribution: "Based on the structure of the Full Stack Deep
    Learning LLM Bootcamp (Spring 2023), by Charles Frye, Sergey Karayev,
    and Josh Tobin (fullstackdeeplearning.com/llm-bootcamp). This is an
    independent structural mirror in original prose; it reproduces no
    course materials, and Full Stack Deep Learning does not endorse it."
This lesson mirrors the structure of the corresponding bootcamp session (LLMOps). Clawdemy's
lessons are original prose taught at a strictly engineering level;
incident-disclosure policy, vendor-failure / SLA / liability, and
compliance-framework debates are out of scope here.

Watch this next

Full Stack Deep Learning, LLM Bootcamp: LLMOps by Charles Frye, Sergey Karayev, and Josh Tobin. The session this lesson mirrors. The recorded version walks the same pillars with concrete tool screenshots.

Going deeper

A short, durable list. Each link is a specific next step, not a generic pile.

LangSmith documentation. A widely-used LLM-observability platform that implements logging, prompt registry, evaluation, and A/B testing in one product. Useful as a reference even if you end up choosing a different platform; the concepts map across.
Langfuse open-source repository. Self-hostable LLM observability + prompt management + evaluation; good if you want to see the LLMOps pillars as code you can run. Worth reading the README and architecture overview.
Honeycomb’s “Observability is the foundation” essay. The classic argument for observability-as-engineering-foundation that LLMOps inherits. Reading it sharpens the why behind the logging discipline this lesson recommends.

Adjacent topics

Where this connects inside the track.

Prompt engineering, “Learn to Spell” (lesson 3). The version-and-test-on-held-out-examples discipline introduced there is the seed of LLMOps regression testing; this lesson grows it into a full practice.
Project walkthrough (lesson 5). The “5-10 log fields per request” naming there is the seed of this lesson’s full observability discipline.
UX for language user interfaces (lesson 6). The “log everything” requirement of recoverable failure feeds directly into the observability pillar here.
What’s next (lesson 8). Phase 3 opens with the frontier-adjacent landscape; LLMOps is the discipline that makes adopting new models in that landscape (next lesson) safe.