Training Trains Internship
Student login
← MLOps learning path
MLOps · Module 10 of 10

MLOps for LLMs and careers

Apply MLOps to generative AI and grow your career.

📖 Key ideas

  1. LLMOps: prompt versioning, evaluation sets, guardrails, cost tracking.
  2. Evaluate LLM outputs with test suites and human review.
  3. Monitor latency, token cost, hallucinations and safety.
  4. Roles: ML engineer, MLOps engineer, AI platform engineer.
  5. Skills: Python, Docker, Kubernetes, cloud, CI/CD, ML basics.
💡 Real-life example: LLMOps is running a busy restaurant kitchen of AI cooks: recipes (prompts), tasting (evals), budget (tokens).
LLMOpsMLOps practices for LLM apps
Prompt versioningTracking prompt changes
GuardrailsSafety checks on inputs/outputs
Token costSpend per input/output tokens