# Two-stage Optimization for Machine Learning Workflow

## TL;DR

This paper introduces a two-stage optimization approach for automating machine learning workflows, focusing on data pipeline construction and algorithm configuration to improve scalability and efficiency.

## Contribution

It proposes a novel two-stage optimization process, including policies for resource allocation and a metric for pipeline independence, enhancing autoML systems.

## Key findings

- Data preprocessing impacts model performance more than hyperparameter tuning.
- Policies effectively balance search time between pipeline construction and algorithm tuning.
- A metric helps identify pipeline independence, aiding in pipeline pruning and meta-learning.

## Abstract

Machines learning techniques plays a preponderant role in dealing with massive amount of data and are employed in almost every possible domain. Building a high quality machine learning model to be deployed in production is a challenging task, from both, the subject matter experts and the machine learning practitioners. For a broader adoption and scalability of machine learning systems, the construction and configuration of machine learning workflow need to gain in automation. In the last few years, several techniques have been developed in this direction, known as autoML. In this paper, we present a two-stage optimization process to build data pipelines and configure machine learning algorithms. First, we study the impact of data pipelines compared to algorithm configuration in order to show the importance of data preprocessing over hyperparameter tuning. The second part presents policies to efficiently allocate search time between data pipeline construction and algorithm configuration. Those policies are agnostic from the metaoptimizer. Last, we present a metric to determine if a data pipeline is specific or independent from the algorithm, enabling fine-grain pipeline pruning and meta-learning for the coldstart problem.