Introduction Through Chapter 4 (Security), we implemented Evals, Observability, and...
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
A practitioner’s guide to machine learning pipeline architecture, MLOps maturity and the tooling that closes the gap between…
In this article, you will learn how to build production-grade LLM systems by following a structured six-step LLMOps roadmap…
Understand the MLOps lifecycle from data preparation to deployment, monitoring, governance, and retraining.
Progettare LLMOps significa scegliere strumenti precisi, non applicare un principio generale. MLflow per il registry, LangChain o…
Example 2: How to implement continuous monitoring in practiceThe powerful principles we’ve discussed (trace logging during…
I've recently participated in the Gemma 4 challenge here on DEV.to, but fell short compared to many...
