Is Your Training Pipeline Production-Ready? A Case Study in the Healthcare Domain

Code

• github.com/spirabr/SPIRA-training-v2
  Officialpytorch★ 1
• github.com/spirabr/SPIRA-training-v1
  Officialpytorch★ 0
• github.com/spirabr/SPIRA-training-v3
  Officialnone★ 0

Abstract

Deploying a Machine Learning (ML) training pipeline into production requires robust software engineering practices. This differs significantly from experimental workflows. This experience report investigates this challenge in SPIRA, a project whose goal is to create an ML-Enabled System (MLES) to pre-diagnose insufficiency respiratory via speech analysis. The first version of SPIRA's training pipeline lacked critical software quality attributes. This paper presents an overview of the MLES, then compares three versions of the architecture of the Continuous Training subsystem, which evolved from a Big Ball of Mud, to a Modular Monolith, towards Microservices. By adopting different design principles and patterns to enhance its maintainability, robustness, and extensibility. In this way, the paper seeks to offer insights for both ML Engineers tasked to productionize ML training pipelines and Data Scientists seeking to adopt MLOps practices.

Reproductions