skills.rest
doanchienthangdev
doanchienthangdevCOMMUNITY
Explore all skills by this author →
GitHub

model-development

Implement machine learning training pipelines with PyTorch, Optuna, and MLflow.

4|1|Updated Dec 30, 2025
Search Tags:#mlops#machine learning#model training#pytorch#hyperparameter tuning#model evaluation#xgboost
One-click install
npx skills add https://github.com/doanchienthangdev/omgkit --skill model-development-doanchienthangdev
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: model-development
Source: https://github.com/doanchienthangdev/omgkit/tree/main/plugin/skills/ml-systems/model-dev
Command: npx skills add https://github.com/doanchienthangdev/omgkit --skill model-development-doanchienthangdev

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill requires scikit-learn, xgboost, lightgbm, catboost, torch, optuna, mlflow, and includes scripts (resource) components.

What problem does it solve?

This Skill addresses the complexities of building, training, and evaluating machine learning models, providing a structured approach to model development.

Core Features & Use Cases

  • Model Selection: Compares performance of various classification models using cross-validation.
  • Training Pipelines: Implements robust training loops for PyTorch models with gradient clipping.
  • Hyperparameter Tuning: Leverages Optuna for efficient and systematic hyperparameter optimization.
  • Model Evaluation: Provides comprehensive metrics including classification reports, confusion matrices, and AUC scores.
  • Model Registry: Integrates with MLflow for logging and registering trained models.
  • Use Case: Develop and deploy a high-performance churn prediction model by systematically selecting the best algorithm, tuning its hyperparameters, and rigorously evaluating its performance.

Quick Start

Use the model-development skill to tune hyperparameters for a new XGBoost model.

Frequently Asked Questions about model-development

High-intent search queries and answers about installing and using this skill.

FAQPage Schema
How do I tune hyperparameters for an XGBoost model using Optuna?▼

Implement a robust training pipeline to train PyTorch models by utilizing training loops with gradient clipping. This approach ensures stable convergence during the machine learning model development lifecycle while preventing exploding gradients.

Can I use MLflow to log and register trained models during development?▼

Yes, you can use MLflow to log and register trained models during development. This integration supports the machine learning model development lifecycle by providing comprehensive versioning and deployment tracking for your evaluated models.

What is the best way to compare multiple classification models before deployment?▼

Evaluate machine learning model performance using comprehensive metrics including classification reports, confusion matrices, and AUC scores. This provides a rigorous assessment of model accuracy and error distribution to address the need for reproducible workflows.

Do I need scikit-learn and PyTorch to implement model training pipelines?▼

No, you do not strictly need both, as the Skill supports multiple gradient boosting libraries like XGBoost, LightGBM, and CatBoost. You can select the best algorithm for your specific use case, such as churn prediction, during the model selection phase.

Related Skills

Explore More Software Engineering Skills→
andikarachmanandikarachman
14

data-preprocessing

Preprocess raw data into clean, analysis-ready datasets with pandas and numpy.

Community
Advanced
nirukk52nirukk52
1

dev-log-monitoring

Monitor Encore.ts and SvelteKit logs to diagnose drift and verify run events.

Community
Advanced
AJBcodingAJBcoding
4

moai-domain-ml

Build ML preprocessing pipelines transforming raw tabular data into model-ready features.

Community
Advanced
KaiserWhoLearnsKaiserWhoLearns

ML Model Training

Train and evaluate machine learning models across scikit-learn, PyTorch, and TensorFlow.

Community
Advanced