A Production ML Workspace
Five parts on the unglamorous scaffolding that makes ML work survive contact with a team: structure, docs, experiments, agents, collaboration.
The work that decides whether an ML project is reproducible a month later, and whether a second person can pick it up at all. Five parts: an organized repository structure, documentation systems that scale, experiment tracking and reproducibility, production-ready agent templates, and the workflow integration that ties a team together.
Read in order for the full workspace, or pull the one part that fixes the gap you have now.
The series
All parts
- shipped8 min
Building a Production ML Workspace, Part 1: Structure
Learn how to design a scalable ML workspace structure that handles Ollama models, fine-tuning, agents, and experiments without becoming chaotic.
- shipped7 min
Building a Production ML Workspace, Part 2: Documentation
A three-tier documentation system that captures ML work for debugging, review, and blog content, turning experiments into shareable knowledge.
- shipped9 min
Building a Production ML Workspace, Part 3: Experiments
Systematic experiment tracking with templates, progress monitoring, and lifecycle management so every ML run is reproducible and builds knowledge.
- shipped10 min
Building a Production ML Workspace, Part 4: Agent Templates
Production-ready AI agents from standardized templates: tool-integration patterns, testing, and deployment-readiness frameworks.
- shipped14 min
Building a Production ML Workspace, Part 5: Collaboration
Finish the workspace with team collaboration patterns, workflow automation, and version-control strategies that scale past one person.
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