Experimentation, deployment and monitoring Machine Learning models: Approaches for applying MLOps

TL;DR

This paper reviews MLOps approaches for automating the entire lifecycle of machine learning models, including experimentation, deployment, and monitoring, highlighting current challenges and solutions in the field.

Contribution

It provides a comprehensive understanding of MLOps techniques and their diverse applications, emphasizing recent developments and ongoing challenges.

Findings

MLOps is a rapidly evolving discipline with various application areas.

Challenges include integrating development and production environments.

Solutions involve automation and continuous monitoring of models.

Abstract

In recent years, Data Science has become increasingly relevant as a support tool for industry, significantly enhancing decision-making in a way never seen before. In this context, the MLOps discipline emerges as a solution to automate the life cycle of Machine Learning models, ranging from experimentation to monitoring in productive environments. Research results shows MLOps is a constantly evolving discipline, with challenges and solutions for integrating development and production environments, publishing models in production environments, and monitoring models throughout the end to end development lifecycle. This paper contributes to the understanding of MLOps techniques and their most diverse applications.