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FlowyML Notebook

Your Analysis. One Reactive Engine.

FlowyML Notebook is a DAG-powered reactive notebook that replaces Jupyter for production ML. Write Python, get automatic dependency tracking, and ship to pipelines — without changing a single line of code.

No cloud lock-in. No JSON diffs. No stale state. Just pure Python notebooks that run, react, and deploy.

🚀 Get Started ✨ Feature Tour

FlowyML Notebook Concept

Reactive DAG engine with integrated AI, data exploration, recipes, deployment, and collaboration

Your Notebook. Reimagined. ⚡

Full Editor

FlowyML Notebook editor — reactive cells, rich outputs, and a professional toolbar

How It Works

  • Reactive DAG

    Every cell is a node in a dependency graph. Change a variable → only dependent cells re-execute. Automatically. Always consistent.

  • Pure Python

    Notebooks are standard .py files. Clean diffs, importable logic, full linting support. No more JSON noise.

  • Rich Exploration

    Every DataFrame gets automatic profiling — stats, charts, correlations, outlier detection, and ML-ready insights. Zero code.

  • Ship to Production

    One-click deploy as API, Docker, web app, or FlowyML pipeline. From notebook to production in seconds.

  • AI Assistant

    Generate code, debug errors, and explain data patterns — context-aware, powered by OpenAI or Google AI.

  • Git-Native

    Full GitHub integration — branch, version, collaborate. No proprietary cloud. No database. Just Git.

  • SmartPrep Advisor

    Auto-detects missing values, skew, outliers, cardinality, and class imbalance — with ready-to-run code fixes.

  • Algorithm Matchmaker

    Ranks the best ML algorithms for your data with reasoning, caveats, and complete sklearn pipeline code.

  • UnicoLab Ecosystem

    Native Keras integration with KDP, KerasFactory, and MLPotion. End-to-end deep learning pipelines from preprocessing to training.

See It In Action

Data Exploration

Automatic chart generation for every column — histograms, distributions, and categorical breakdowns

Pipeline DAG

Visual dependency graph: imports → data_generation → analysis → exploration → summary

Built For

  • Data Scientists

    Rich exploration, reactive execution, and ML insights — the notebook you wished Jupyter was.

  • ML Engineers

    Pipeline promotion, asset tracking, and deployment — ship models from your notebook to production.

  • Teams

    GitHub collaboration, shared recipes, inline comments — work together without cloud vendor lock-in.

  • Enterprises

    Self-hosted, API-driven, FlowyML ecosystem integration — production-grade infrastructure from day one.

Why Switch From Jupyter?

Jupyter FlowyML Notebook
Execution Run cells in any order → stale state Reactive DAG → always consistent
File format .ipynb JSON → merge nightmare .py files → clean Git diffs
Collaboration None built-in GitHub-native branching & versioning
Deployment Copy-paste to scripts One-click pipeline, API, or app
Data exploration Raw text output Rich profiling, charts, ML insights
Preprocessing help None SmartPrep Advisor with code generation
Algorithm selection Trial and error Algorithm Matchmaker with ranked recommendations
Code reuse Copy between notebooks 43 recipes + Collaborative Analysis Patterns + Ecosystem recipes

Quick Start

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pip install "flowyml-notebook[all]"
fml-notebook start

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Open Source. Apache 2.0 Licensed. Self-hosted. No vendor lock-in.

View on GitHub PyPI Package