MLOps Spanning Whole Machine Learning Life Cycle: A Survey

TL;DR

This survey provides a comprehensive overview of MLOps, the process encompassing the entire machine learning lifecycle, highlighting key concepts, technologies, and recent advances to aid newcomers and practitioners.

Contribution

It offers a structured, survey-based overview of MLOps, consolidating diverse technologies and concepts into a clear, accessible reference for understanding the ML lifecycle.

Findings

Provides a structured overview of MLOps concepts and activities.

Summarizes key technologies used in each step of ML lifecycle.

Serves as a quick reference for ML practitioners and newcomers.

Abstract

Google AlphaGos win has significantly motivated and sped up machine learning (ML) research and development, which led to tremendous ML technical advances and wider adoptions in various domains (e.g., Finance, Health, Defense, and Education). These advances have resulted in numerous new concepts and technologies, which are too many for people to catch up to and even make them confused, especially for newcomers to the ML area. This paper is aimed to present a clear picture of the state-of-the-art of the existing ML technologies with a comprehensive survey. We lay out this survey by viewing ML as a MLOps (ML Operations) process, where the key concepts and activities are collected and elaborated with representative works and surveys. We hope that this paper can serve as a quick reference manual (a survey of surveys) for newcomers (e.g., researchers, practitioners) of ML to get an overview of the MLOps process, as well as a good understanding of the key technologies used in each step of the ML process, and know where to find more details.