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Philosophy

Crew mission patch

Who this guide is for, and how we chose the tools.

Target audience

This guide is for SMEs and small teams with limited dedicated MLOps infrastructure who want to bring ML projects from notebooks to production without a heavy, monolithic platform.

It is especially aimed at two profiles:

  • Data scientists who train models in notebooks, save artifacts manually, and deploy with ad-hoc scripts. The guide gives you practical, incremental steps toward reproducibility, automation, deployment, monitoring, and retraining.

  • Software engineers moving into ML engineering who already know DevOps practices. The guide shows how to extend those practices to ML concerns such as data versioning, experiment reporting, and drift detection.

Our principles

We believe MLOps should be:

  • Version-controlled: track code, parameters, and deployments in Git, and keep data versions linked to them, so every model can be reproduced.
  • Composable: use best-of-breed open-source tools that each solve one problem well.
  • Incremental: adopt one practice at a time, not all at once.
  • Pragmatic: prioritize reproducibility first, then automation, then deployment, then monitoring, then feedback loops.

That is why we avoid all-in-one MLOps platforms that require dedicated infrastructure or databases. A lightweight, Git-native stack gives you a pragmatic path from notebooks to production while staying in control of your tooling.