Mle Workflow
affaan-m/everything-claude-codeMachine learning models need structure to work reliably in production. This workflow helps you build data contracts, repeatable training, and measurable quality checks. It turns notebook experiments into systems that can be deployed, monitored, and rolled back.
You can use it when planning a new model feature or refreshing an existing one. It works for ranking, recommendations, classifiers, forecasting, and more. But it does not force one architecture onto every project.
The skill also connects with standard software engineering practices. Use it alongside code review, testing, and deployment patterns to build robust ML systems.
Add Mle Workflow skill to your workflow
Global
mkdir -p ~/.claude/skills/mle-workflowProject
mkdir -p .claude/skills/mle-workflowSource Repository
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