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Guide to prepare MLOps projects for sharing, collaboration, and community engagement.
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Basé sur la classification professionnelle SOC
Guide to refine MLOps projects with task automation, containerization, CI/CD pipelines, and robust experiment tracking.
Guide to transform prototypes into robust, distributable Python packages using the src layout, hybrid paradigm, and strict configuration management.
Guide to initialize a new MLOps project with standard tools (uv, git, VS Code) and best practices.
Guide to implement full stack observability including reproducibility, lineage, monitoring, alerting, and explainability.
Guide to create structured, reproducible Jupyter notebooks for MLOps prototyping, emphasizing configuration management and pipeline integrity.
Guide to implement rigorous validation layers including static analysis, automated testing, structured logging, and security scanning.
| name | MLOps Collaboration |
| description | Guide to prepare MLOps projects for sharing, collaboration, and community engagement. |
To transform a private project into a public, collaborative resource by establishing Governance (License, Code of Conduct), Documentation (README, Contributing), Standardization (Templates, Workstations), and Release Management.
Set the rules of engagement.
CODE_OF_CONDUCT.md to foster a safe community.main branch (require PRs, status checks)..gemini/config.yaml)..gitignore (exclude secrets, data, venvs).Make the project usable and understandable.
README.md gets too long.Eliminate "it works on my machine".
cookiecutter for scaffolding and cruft update to keep projects synced..devcontainer/devcontainer.json.
Ship with confidence.
main is stable).develop + release branches).LICENSE file present?README.md have installation instructions?CONTRIBUTING.md clear?.devcontainer/devcontainer.json exist?