moai-domain-ml

What problem does it solves? This Skill provides expert guidance for the entire machine learning lifecycle, from model development and training to deployment and MLOps. It addresses challenges in model evaluation, hyperparameter tuning, and production workflows, helping you build reliable, scalable, and maintainable ML systems.

Core Features & Use Cases

• Model Training: Guides through using scikit-learn, TensorFlow/Keras, PyTorch, and XGBoost for various ML tasks.
• Model Evaluation: Provides expertise in metrics for classification (accuracy, precision, recall) and regression (RMSE, MAE), along with cross-validation.
• MLOps Workflows: Integrates concepts like experiment tracking (MLflow), feature stores (Feast), and model registries for production ML systems.
• Use Case: When you need "머신러닝 모델 개발" (machine learning model development) or "모델 배포" (model deployment) for a new predictive service, this skill offers the necessary guidance.

Quick Start

Ask to "머신러닝 모델 개발" for a classification task, and the skill will guide you through training with scikit-learn and cross-validation.