#160160th of 168 in ML frameworks and libraries
transmogrifai
Scala AutoML library on Apache Spark for structured data
- Stars
- 2.3k
- License
- BSD-3-Clause
- Last commit
- Jun 2026
- Last release
- Jun 2020
- Language
- Scala
2,263 on 21 Apr 20262,281 on 10 Nov 2026
From GitHub's daily star counts.
Checked on GitHub.
Overview
TransmogrifAI is a Scala library that runs on Apache Spark and automates feature engineering, feature validation and model selection for structured data. Its API is type-checked at compile time, and workflows are built from reusable stages. Users can accept the automated defaults or specify every feature and algorithm by hand.
Who it is for: Scala/Spark teams building ML models on tabular data
Strengths
- One-line feature engineering, sanity checks and model selection on Spark DataFrames
- Compile-time type-safe feature API with modular, reusable workflow stages
- Model summary reports metrics, chosen hyperparameters and feature insights (correlation, CramersV)
- Automation is optional: features and algorithms can be specified explicitly
Weaknesses
- Last release 0.7.0 in June 2020; targets Spark 2.4 and Scala 2.11 only
- Requires Java 1.8 and a Spark/Scala stack, a poor fit for Python teams
- Aimed at structured data; no LLM or deep learning support described
- README example only shows logistic regression and random forest selection
What it needs
- no GPU
- Needs Apache Spark 2.4, Scala 2.11, Java 1.8
- Models: Spark MLlib logistic regression, Spark MLlib random forest
Quick answers
Is transmogrifai open source?
Yes. Its code is on GitHub under the BSD-3-Clause license.
Does transmogrifai need a GPU?
No, it runs without one.
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