# AutoML @ NeurIPS 2018 challenge: Design and Results

## TL;DR

This paper reports on the NeurIPS 2018 challenge focused on developing algorithms for autonomous lifelong machine learning in non-i.i.d. environments, highlighting the competition design and key outcomes.

## Contribution

It introduces a large-scale competition framework for lifelong learning with drift, providing insights into current capabilities and challenges in this area.

## Key findings

- Over 300 participants engaged in the challenge
- Development of algorithms capable of handling non-i.i.d. data
- Insights into effective strategies for lifelong learning with drift

## Abstract

We organized a competition on Autonomous Lifelong Machine Learning with Drift that was part of the competition program of NeurIPS 2018. This data driven competition asked participants to develop computer programs capable of solving supervised learning problems where the i.i.d. assumption did not hold. Large data sets were arranged in a lifelong learning and evaluation scenario and CodaLab was used as the challenge platform. The challenge attracted more than 300 participants in its two month duration. This chapter describes the design of the challenge and summarizes its main results.