/ml--mlops-pipeline

/ml--mlops-pipeline is a Claude Code skill in the AI & Agents section. Gives an ML project reproducible training, experiment tracking, a model registry and CI, sized to the team instead of a full platform.

Author's description

Set up MLOps — experiment tracking, model registry, CI for models, reproducibility

How to ask for it

/ml--mlops-pipeline set up a model registry for my team
/ml--mlops-pipeline add experiment tracking to my project
/ml--mlops-pipeline configure CI to validate models before deploy

Install

curl -fsSL https://raw.githubusercontent.com/sgomez-dev/claude-skills/main/install.sh | bash

After installing with the script, type /ml--mlops-pipeline. Using Cursor, Windsurf or Codex? Platform guides

Demo coming soon

/ml--model-deploymentDeploying an ML model
Takes a trained model to production: batch or online serving pattern, prediction API, packaging, monitoring, drift detection and rollback plan.
Built here
/ml--model-evaluationML model evaluation
Evaluates a model beyond one headline number: metrics matched to error costs, calibration, per-slice performance and error-by-error analysis.
Built here
/ml--model-trainingTraining an ML model
Trains a model methodically: a trivial baseline first, then the simplest model that could work, with cross-validation and tuning.
Built here
/ml--recommender-systemRecommender system
Builds a recommender that beats a popularity baseline: picks the approach, handles cold start and evaluates it offline.
Built here
/ml--time-series-forecastTime series forecasting
Builds a forecast with naive baselines, seasonality, backtesting and prediction intervals, without leaking future data.
Built here
/agent-reachWeb and social media research
Searches and reads content from the web and 16 platforms (Reddit, YouTube, X, LinkedIn, GitHub, RSS…) to research a topic. Read-only: it never posts.
By Panniantong

How to ask for it

Install

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