mlcli
DocsRunsReleasesUI Demo
Get Started
  • Introduction
  • Quickstart
  • Examples
  • Config Reference
  • Overview
  • Gradient Boosting
  • Clustering
  • Anomaly Detection
  • Overview
  • Overview
  • Overview
  • Tracker Overview
  • CLI Commands

Need help?

Check our GitHub for issues and discussions.

View on GitHub →
v0.3.0 Now Available

Documentation

Learn how to use mlcli to train, evaluate, and manage machine learning models from the command line with ease.

Getting Started

Install mlcli and run your first training job in minutes.

Configuration

Learn about config files, environment variables, and customization.

Trainers

Explore 15+ model trainers from scikit-learn, XGBoost, and TensorFlow.

Preprocessing

Data preprocessing pipelines, scalers, encoders, and feature selection.

Experiment Tracking

Track experiments, compare runs, log metrics, and visualize results.

Model Explainability

Understand your models with SHAP, LIME, and feature importance.

Quick Install

Get started with mlcli in seconds. Install via pip and you're ready to train your first model.

$ pip install mlcli-toolkitGet Started

Need Help?

Can't find what you're looking for? We're here to help.

DiscussionsReport Issue

Footer

mlcli

A production-ready CLI tool for training, evaluating, and managing machine learning models with experiment tracking and hyperparameter tuning.

Product

  • Features
  • Quickstart
  • Releases
  • Download

Resources

  • Documentation
  • API Reference
  • Examples
  • UI Demo

Community

  • GitHub
  • Issues
  • Discussions
  • Contribute

Legal

  • License
  • About

© 2026 mlcli. Open source under MIT License.

Made with for the ML community