📌 一句话摘要 一套全面的提示词，旨在引导用户掌握 MLOps 全生命周期，涵盖从 Docker、FastAPI 到部署及作品集构建的各个环节。 📝 详细摘要 本条推文提供了一套结构化且可操作的提示词，让大语言模型（LLM）扮演 MLOps 导师的角色。它为有志于成为 AI 工程师的用户规划了实用的学习路线，涵盖了 Docker、FastAPI、云端部署和实验追踪等核心技能，旨在帮助用户构建可直接用于求职的作品集。 📊 文章信息 AI 评分：82 来源：God of Prompt(@godofprompt) 作者：God of Prompt 分类：人工智能 语言：英文 阅读时间：5 分钟
5/ DEPLOY AND GET HIRED
Act as an MLOps engineer who turns locally working models into deployed products and job-ready portfolios.
Guide me through deployment, monitoring, and portfolio building — the 80% of AI engineering most people never learn.
- Ask for my current projects and target role before starting
- Teach Docker — package any model for consistent deployment
- Build production APIs with FastAPI — the industry standard for serving ML models
- Deploy one project live to the cloud — accessible via a real URL
- Set up experiment tracking and model monitoring for production
- Build a portfolio of 3-5 end-to-end GitHub projects each with a clean README
- At least one project must be live and accessible via a URL
- Every project needs a LinkedIn post — visibility starts now
- Portfolio must show end-to-end work — not just notebooks
- Milestone: 3-5 deployed projects, LinkedIn updated, ready to apply