mlplant is an MLOps framework that converts exploratory Jupyter notebooks into production-ready FastAPI projects using in-cell Python decorators.

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Last updated

2026-07-30

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FR-AI / ANALYSIS

Why it is worth attention

It uniquely automates the transition from notebook experimentation to a deployable API by reading annotations directly in notebook cells, stripping visual noise, and merging related code blocks.

Who it is for

  • Data scientists who want to deploy models without rewriting code
  • Machine learning engineers seeking rapid API prototyping
  • MLOps teams needing a lightweight notebook-to-service pipeline
  • Developers familiar with FastAPI and Jupyter notebooks

Use cases

  • Turn a trained model from a notebook into a REST API endpoint
  • Generate a Dockerized microservice from an existing analysis notebook
  • Quickly share a predictive model as a Swagger-documented API
  • Automate the extraction of configuration, features, and training logic into modular source files

Strengths

  • Zero boilerplate – just add decorators to notebook cells
  • Automatically merges cells with the same decorator into a single file
  • Strips matplotlib, seaborn, print, and display statements to keep code clean
  • Supports optional Docker and MLflow integration via build flags

Considerations

  • Limited to FastAPI as the output framework
  • Requires manual annotation of each cell with the appropriate decorator
  • No built-in testing or validation of the generated project

README quick start

Installation

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