SE-ML/awesome-seml 源码下载与部署教程

项目概述

A curated list of articles that cover the software engineering best practices for building machine learning applications.

项目地址

https://github.com/SE-ML/awesome-seml

项目页面预览

关键指标

• Stars：1333
• 主要语言：
• License：Creative Commons Zero v1.0 Universal
• 最近更新：2024-03-26T22:53:29Z
• 默认分支：master

本站高速下载（国内可用）

当前未生成本站压缩包（稍后重试）。

安装部署要点（README 精选）

Deployment and Operation

How to deploy and operate your models in a production environment.

• Best Practices in Machine Learning Infrastructure
• Building Continuous Integration Services for Machine Learning 🎓
• Continuous Delivery for Machine Learning ⭐
• Continuous Training for Production ML in the TensorFlow Extended (TFX) Platform 🎓
• Fairness Indicators: Scalable Infrastructure for Fair ML Systems 🎓
• Machine Learning Logistics
• Machine learning: Moving from experiments to production
• ML Ops: Machine Learning as an engineered disciplined
• Model Governance Reducing the Anarchy of Production 🎓
• ModelOps: Cloud-based lifecycle management for reliable and trusted AI
• Operational Machine Learning
• Scaling Machine Learning as a Service🎓
• TFX: A tensorflow-based Production-Scale ML Platform 🎓
• The ML Test Score: A Rubric for ML Production Readiness and Technical Debt Reduction 🎓
• Underspecification Presents Challenges for Credibility in Modern Machine Learning 🎓
• Versioning for end-to-end machine learning pipelines 🎓

常用命令（从 README 提取）

（未提取到命令块）

通用部署说明

1. 下载源码并阅读 README
2. 安装依赖（pip/npm/yarn 等）
3. 配置环境变量（API Key、模型路径、数据库等）
4. 启动服务并测试访问
5. 上线建议：Nginx 反代 + HTTPS + 进程守护（systemd / pm2）

免责声明与版权说明

本文仅做开源项目整理与教程索引，源码版权归原作者所有，请遵循对应 License 合规使用。