fmindCommunity·7 Agent Skills Included

mlops-python-package

Production-ready MLOps package for training, tracking, and deploying ML models

Builds a complete MLOps pipeline covering model tuning, training, promotion, inference, evaluation, and explanation. Eliminates messy notebooks and untracked experiments by enforcing MLflow tracking, YAML configs, and full test coverage. Ships with Docker, CI/CD, and quality gates so teams can ship reliable ML packages faster.

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npx skills add fmind/mlops-python-package --all -g -y

Available:

Gives your AI agent the project stack, task commands, coding conventions, and definition of done so it can modify and run the MLOps pipeline correctly.

All Skills in This Repository (7)

Pure Emerald Level Indicators

📦 In Repo

fmind

MLOps Observability

Configure ML observability with MLflow, Evidently, and SHAP for drift detection and alerting.

MLOps Industrialization

Convert prototype notebooks into distributable Python packages with src layout.

MLOps Prototyping

Create structured, reproducible MLOps prototyping notebooks in Jupyter.

MLOps Validation

Enforce Python code quality and security with automated validation layers.

MLOps Automation

Scaffold MLOps projects with Justfile, Dockerfile, GitHub Actions, and MLflow.

MLOps Initialization

Automate MLOps project bootstrap with uv, git, and VS Code configuration.

MLOps Collaboration

Transform private MLOps projects into public resources with governance and release workflows.

Community

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Frequently Asked Questions

FAQPage Schema

How to install mlops-python-package?▼

Run `npx skills add fmind/mlops-python-package --all -g -y` in your terminal to install all skills in this suite globally.

What does this MLOps package do?▼

It provides a production-shaped Python package that runs the full ML lifecycle: tuning, training, model promotion, inference, evaluation, and explanation, all tracked with MLflow.

How do I run training and inference jobs?▼

Each job is defined by a YAML file in the confs folder, and you run it with a single command like `uv run bikes confs/training.yaml`.

Does it work with AI coding agents?▼

Yes. The AGENTS.md file gives AI agents the full stack context, task commands, and conventions needed to work on the repository autonomously.

Can I deploy models with Docker using this package?▼

Yes. It includes a Dockerfile, docker-compose setup, and MLflow Projects support so you can build and ship reproducible model images.