machine-learning

What problem does it solve?

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.

Core Features & Use Cases

• ML Lifecycle Guidance: from problem definition to production monitoring
• Data Preparation & Feature Engineering: recommended patterns and checks
• Model Training & Evaluation: experiment design, metrics selection, and selection criteria
• Production Deployment: serving strategies and monitoring
• Experiment Tracking: versioning, lineage, and reproducibility

Quick Start

Plan an ML experiment to forecast demand: define problem type, select metrics, and outline a training and evaluation plan.