Welcome to ML Ops Quickstart

ML Ops Quickstart is a tool for initializing Machine Learning projects following ML Ops best practices.

Setting up new repositories is a time-consuming task that involves creating different files and configuring tools such as linters, docker containers and continuous integration pipelines. The goal of mloq is to simplify that process, so you can start writing code as fast as possible.

mloq generates customized templates for Python projects with focus on Maching Learning. An example of the generated templates can be found in mloq-template.

Welcome to MLOQ

• About ML Ops Quickstart
• Installation
  • Install from pypi
  • Install from source

How to

• Command line interface
• Usage examples
• mloq.yml config file

Features

• Repository files
• Packaging
• Code style
• Requirements
• Docker
• Continuous integration using GitHub Actions
• Testing
• Project Makefile

MLOQ Library

• License
• Contributing
• Contributing Guidelines
  • Certificate of Origin
  • Support Channels
  • How to Contribute
    • Format of the commit message
  • Roadmap
• API reference
  • mloq
    • Subpackages
      • mloq.commands
        • Submodules
        • Package Contents
      • mloq.config
        • Submodules
    • Submodules
      • mloq.__main__
      • mloq._utils
        • Module Contents
      • mloq.cli
        • Module Contents
      • mloq.command
        • Module Contents
      • mloq.custom_click
        • Module Contents
      • mloq.failure
        • Module Contents
      • mloq.files
        • Module Contents
      • mloq.git
        • Module Contents
      • mloq.record
        • Module Contents
      • mloq.runner
        • Module Contents
      • mloq.templating
        • Module Contents
      • mloq.version
        • Module Contents
      • mloq.writer
        • Module Contents
    • Package Contents
      • _logger

Indices and tables

Index Module Index Search Page