Plan production ML systems with data contracts, evaluation gates, and rollback criteria.
npx skills add https://github.com/WUIX69/e-lock --skill mle-workflow-wuix69
Please help me install this Agent Skill. Skill: mle-workflow Source: https://github.com/WUIX69/e-lock/tree/main/.agent/.agents/skills/mle-workflow Command: npx skills add https://github.com/WUIX69/e-lock --skill mle-workflow-wuix69
This Skill turns ambiguous machine-learning work into a production-ready system with clear contracts, reproducible training, measurable quality gates, and safe rollout paths.
Use the mle-workflow skill to draft a production ML plan for your model, including data contracts, evaluation gates, serving requirements, monitoring checks, and rollback criteria.
High-intent search queries and answers about installing and using this skill.
To move a machine learning model to production, you need explicit data contracts, reproducible training, promotion gates, and deployment safeguards. This workflow packages serving artifacts with validation, fallback behavior, and rollback plans to ensure safe production rollout.
Data contracts in MLOps define the target decision, entity grain, label timing, feature freshness, and point-in-time join rules before implementation. You need them to keep machine learning behavior testable, reviewable, and reproducible across training and serving environments.
Reproducible training and evaluation require standardizing configs, seeds, dataset versions, metrics, slice checks, and promotion gates. This ensures machine learning results can be repeated and reviewed before a model moves to serving or deployment.
Yes, this workflow applies to deploying LLM workflows, recommenders, rankers, classifiers, and forecasts. It handles model development, refreshes, evaluation, serving, monitoring, and rollback planning for diverse machine learning systems.
The best way to monitor ML models post-launch is to package them with operational monitoring checks for drift and quality. This workflow defines post-launch drift and quality monitoring alongside rollback criteria to maintain reliable ML behavior.
ML model behavior changes between training and serving due to a lack of train-serve parity and missing point-in-time join rules. Defining explicit data contracts and feature freshness requirements before implementation prevents this divergence.
aloneggAutomate end-to-end ML analysis from raw data to evaluated models.
nazrulsoftwaredevStandardize the ML engineering lifecycle with data contracts, training pipelines, and promotion gates.
boparaiamritAudit CI/CD pipelines for automation, testing, rollback, and secrets management.
guccangExecute Python code in a persistent Jupyter kernel session.
