MLOps / LLMOps — CI/CD Pipelines for Continuous Quality Assurance
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Introduction Through Chapter 4 (Security) , we implemented Evals, Observability, and Security as individual components. In this chapter, we integrate them into a system for continuous operations. LLMOps shares DNA with MLOps but faces fundamentally different challenges. Prompts are code, Evals replace unit tests, provider switching is routine, and costs are unpredictable. [Before] Manually executed scripts python evals/eval_rag.py ← run by hand python security/secure_rag.py ← run by hand [Now —…
1Key Takeaways
- Introduction Through Chapter 4 (Security) , we implemented Evals, Observability, and Security as individual components.
- In this chapter, we integrate them into a system for continuous operations.
- LLMOps shares DNA with MLOps but faces fundamentally different challenges.
- Prompts are code, Evals replace unit tests, provider switching is routine, and costs are unpredictable.
2AIWedia Score
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3Why it matters
Coding AI shifts how fast software ships and how much human review each change needs. DEV — AI reports that introduction Through Chapter 4 (Security) , we implemented Evals, Observability, and Security as individual components.
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