ml-workflow

What problem does it solve?

This Skill provides a structured and systematic approach to machine learning model development, ensuring best practices are followed from experiment design to deployment.

Core Features & Use Cases

• Experiment Design: Define clear hypotheses, metrics, and success criteria for ML experiments.
• Baseline Establishment: Quickly set up and evaluate baseline models for performance comparison.
• Iterative Improvement: Systematically track and analyze experiments to drive model enhancements.
• Experiment Tracking: Utilize tools like MLflow for logging parameters, metrics, and models.
• Use Case: A data scientist needs to develop a new churn prediction model. This Skill guides them through setting up initial baselines, designing experiments to test new features, tracking results with MLflow, and iterating towards a production-ready model.

Quick Start

Use the ml-workflow skill to design an experiment for improving model accuracy on the customer churn dataset.