# Interpretable Automated Machine Learning in Maana(TM) Knowledge Platform

arXiv: https://arxiv.org/abs/1905.02168 · HTML: https://mlsanity.com/paper/1905.02168/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 6 May 2019.

## Built on by (most cited first, 2 of 2)

- [Evolving machine learning workflows through interactive AutoML](https://mlsanity.com/paper/2402.18505/) (2024)
- [Grammar-based evolutionary approach for automated workflow composition with domain-specific operators and ensemble diversity](https://mlsanity.com/paper/2402.02124/) (2024)

## Abstract (arXiv)

Machine learning is becoming an essential part of developing solutions for many industrial applications, but the lack of interpretability hinders wide industry adoption to rapidly build, test, deploy and validate machine learning models, in the sense that the insight of developing machine learning solutions are not structurally encoded, justified and transferred. In this paper we describe Maana Meta-learning Service, an interpretable and interactive automated machine learning service residing in Maana Knowledge Platform that performs machine-guided, user assisted pipeline search and hyper-parameter tuning and generates structured knowledge about decisions for pipeline profiling and selection. The service is shipped with Maana Knowledge Platform and is validated using benchmark dataset. Furthermore, its capability of deriving knowledge from pipeline search facilitates various inference tasks and transferring to similar data science projects.