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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

  1. shipped8 min

    Building a Production ML Workspace: Part 1 - Designing an Organized Structure

    Learn how to design a scalable ML workspace structure that handles Ollama models, fine-tuning, agents, and experiments without becoming chaotic.

  2. shipped7 min

    Building a Production ML Workspace: Part 2 - Documentation Systems That Scale

    Build a three-tier documentation system that captures ML work for debugging, review, and blog content—turning your experiments into shareable knowledge.

  3. shipped9 min

    Building a Production ML Workspace: Part 3 - Experiment Tracking and Reproducibility

    Build systematic experiment tracking with templates, progress monitoring, and lifecycle management to ensure every ML experiment is reproducible and builds toward knowledge.

  4. shipped10 min

    Building a Production ML Workspace: Part 4 - Production-Ready AI Agent Templates

    Build production-ready AI agents with standardized templates, tool integration patterns, comprehensive testing, and deployment readiness frameworks.

  5. shipped14 min

    Building a Production ML Workspace: Part 5 - Team Collaboration and Workflow Integration

    Complete your production ML workspace with team collaboration patterns, workflow automation, version control strategies, and integration frameworks that scale.

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