TransmogrifAI is an end-to-end Auto-ML library for structured data written in Scala that runs on top of Apache Spark, an open-source unified analytics engine for large-scale data processing. It was developed with a focus on accelerating machine learning developer productivity through machine learning automation, and an API that enforces compile-time type-safety, modularity, and reuse.
For automation, TransmogrifAI has numerous Transformers and Estimators that make use of Feature abstractions to automate feature engineering, feature validation, and model selection.
For modularity and reuse, TransmogrifAI enforces a strict separation between ML workflow definitions and data manipulation, ensuring that code written using TransmogrifAI is inherently modular and reusable.
For compile-time type-safety, machine learning workflows built using TransmogrifAI are strongly typed. This means developers get to enjoy the many benefits of compile-time type safety, including code completion during development and fewer runtime errors.
For transparency, model insights leverage stored feature metadata and lineage to help debug models while providing insights to the end user, making machine learning models less of a black box.
Link: https://transmogrif.ai/
View Resource