Towards Trustworthy Machine Learning in Production: An Overview of the Robustness in MLOps Approach

TL;DR

This paper provides a comprehensive overview of robust MLOps practices, tools, and research approaches to enhance the trustworthiness and reliability of machine learning systems in real-world production environments.

Contribution

It surveys existing research, tools, and challenges related to robustness in MLOps, offering guidance for deploying trustworthy ML solutions in practice.

Findings

Identifies key technical practices for robust MLOps

Reviews tools supporting robustness in MLOps systems

Highlights open challenges and future research directions

Abstract

Artificial intelligence (AI), and especially its sub-field of Machine Learning (ML), are impacting the daily lives of everyone with their ubiquitous applications. In recent years, AI researchers and practitioners have introduced principles and guidelines to build systems that make reliable and trustworthy decisions. From a practical perspective, conventional ML systems process historical data to extract the features that are consequently used to train ML models that perform the desired task. However, in practice, a fundamental challenge arises when the system needs to be operationalized and deployed to evolve and operate in real-life environments continuously. To address this challenge, Machine Learning Operations (MLOps) have emerged as a potential recipe for standardizing ML solutions in deployment. Although MLOps demonstrated great success in streamlining ML processes, thoroughly defining the specifications of robust MLOps approaches remains of great interest to researchers and practitioners. In this paper, we provide a comprehensive overview of the trustworthiness property of MLOps systems. Specifically, we highlight technical practices to achieve robust MLOps systems. In addition, we survey the existing research approaches that address the robustness aspects of ML systems in production. We also review the tools and software available to build MLOps systems and summarize their support to handle the robustness aspects. Finally, we present the open challenges and propose possible future directions and opportunities within this emerging field. The aim of this paper is to provide researchers and practitioners working on practical AI applications with a comprehensive view to adopt robust ML solutions in production environments.