Automate the machine learning lifecycle from experimentation to production deployment.
npx skills add https://github.com/89jobrien/steve --skill machine-learning
Please help me install this Agent Skill. Skill: machine-learning Source: https://github.com/89jobrien/steve/tree/main/steve/skills/machine-learning Command: npx skills add https://github.com/89jobrien/steve --skill machine-learning
Building and maintaining machine learning systems requires structured processes across problem framing, data prep, model training, evaluation, deployment, and monitoring. This Skill provides a comprehensive framework to navigate the ML lifecycle with repeatable patterns.
Plan an ML experiment to forecast demand: define problem type, select metrics, and outline a training and evaluation plan.
High-intent search queries and answers about installing and using this skill.
An ML pipeline automates data preprocessing, feature engineering, model training, evaluation, and deployment in a reproducible workflow. This guidance covers problem framing, metric selection, experiment tracking with versioning, and serving strategies across structured, image, and text data to meet production requirements.
Structured model training requires experiment design with clear metrics selection, hyperparameter tuning strategies, and evaluation criteria. Version your artifacts and track lineage across runs to ensure reproducibility, enable comparison across experiments, and support deployment decisions with validated results.
Production monitoring detects when input data distributions shift or model performance declines over time. Implement tracking for data drift and model metrics post-deployment to trigger retraining, maintain model quality, and catch issues before they impact predictions.
Yes, this lifecycle framework applies to structured tabular data, image data, and text data. The same problem definition, data preparation, model training, evaluation, deployment, and monitoring patterns scale across domains while maintaining reproducibility and versioning.
Data preparation includes validation checks and recommended preprocessing patterns. Feature engineering transforms raw data into model-ready features; following structured patterns ensures data quality, reproducibility, and scalable training across experiments.
CI/CD integration automates model validation, versioning, and deployment workflows. This enables repeatable, scalable serving patterns and connects experiment tracking to production deployments while maintaining artifact lineage and monitoring integration.
mvillmowPrepare and validate datasets for machine learning model training.
nirukk52Monitor Encore.ts and SvelteKit logs to diagnose drift and verify run events.
manutejAutomate ML lifecycle management with MLflow experiment tracking and model registry.
pluginagentmarketplaceBuild and deploy machine learning pipelines with Python, Scikit-learn, TensorFlow, and PyTorch.
