End-to-End LLM Engineering and Deployment Pipeline

ML & AI · flowchart diagram · MIT

This flowchart illustrates a comprehensive pipeline for developing, training, optimizing, and deploying Large Language Models (LLMs), from raw data to a ve

Source: https://github.com/jiaran-king/MicroLM/blob/782ae02f10c14b484a317f22115a066b3b10b91d/Readme/%E9%A1%B9%E7%9B%AE%E5%85%A8%E6%99%AF%E5%9B%BE/00-%E5%85%A8%E6%B5%81%E7%A8%8B%E5%88%86%E6%9E%90%EF%BC%88%E8%AE%AD%E7%BB%83%E3%80%81%E6%8E%A8%E7%90%86%E3%80%81%E8%AF%84%E6%B5%8B%E4%B8%8E%E9%83%A8%E7%BD%B2%EF%BC%89.md
Curated by jiaran-king
LLM AI Machine Learning Deployment Training Evaluation vLLM

Mermaid source

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flowchart TB
    C1["原始数据"] --> C2["tokenizer / 数据 pipeline"]
    C2 --> C3["pretrain / SFT / LoRA"]
    C3 --> C4["推理优化 / 对话系统"]
    C4 --> C5["自动化评测"]
    C5 --> C6["模型导出"]
    C6 --> C7["vLLM 部署"]
    C7 --> C8["smoke / benchmark / stability 验证"]

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    class C1,C2,C3,C4,C5,C6,C7,C8 chain;

What this diagram shows

This diagram outlines the complete lifecycle of an LLM project, starting from raw data processing, through tokenizer and data pipeline creation, various training stages (pretrain, SFT, LoRA), inference optimization, dialogue system integration, automated evaluation, model export, vLLM deployment, and finally, verification steps including smoke tests, benchmarks, and stability checks.

When to use it

Use this diagram when planning, designing, or documenting an end-to-end LLM development and deployment workflow. It's ideal for understanding the full scope of an LLM engineering project, from data ingestion to production readiness.

How to adapt it for your project

This pipeline can be adapted by incorporating different data sources, alternative tokenizers or data processing frameworks, various fine-tuning techniques beyond LoRA, other inference optimization strategies, different deployment platforms (e.g., Kubernetes, serverless), or specialized evaluation metrics tailored to specific LLM applications.

Key concepts