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An Empirical Evaluation of Modern MLOps Frameworks

Jon Marcos-Mercadé, Unai Lopez-Novoa, Mikel Egaña Aranguren

TL;DR

This study addresses the challenge of selecting MLOps frameworks by empirically comparing MLflow, Metaflow, Apache Airflow, and Kubeflow Pipelines through two representative pipelines (MNIST and IMDB with distilBERT). It uses a weighted rubric across six usability and interoperability criteria and deploys the tools in mixed environments to reveal practical trade-offs. The findings show MLflow excels in ease of use and interpretability, Airflow leads in orchestration and ecosystem support, Metaflow offers a superior developer experience for rapid prototyping, and Kubeflow Pipelines targets production-grade Kubernetes deployments with higher setup costs. The results provide actionable guidance for teams based on infrastructure maturity and objectives, and outline concrete directions for extending evaluations to more production-like scenarios.

Abstract

Given the increasing adoption of AI solutions in professional environments, it is necessary for developers to be able to make informed decisions about the current tool landscape. This work empirically evaluates various MLOps (Machine Learning Operations) tools to facilitate the management of the ML model lifecycle: MLflow, Metaflow, Apache Airflow, and Kubeflow Pipelines. The tools are evaluated by assessing the criteria of Ease of installation, Configuration flexibility, Interoperability, Code instrumentation complexity, result interpretability, and Documentation when implementing two common ML scenarios: Digit classifier with MNIST and Sentiment classifier with IMDB and BERT. The evaluation is completed by providing weighted results that lead to practical conclusions on which tools are best suited for different scenarios.

An Empirical Evaluation of Modern MLOps Frameworks

TL;DR

This study addresses the challenge of selecting MLOps frameworks by empirically comparing MLflow, Metaflow, Apache Airflow, and Kubeflow Pipelines through two representative pipelines (MNIST and IMDB with distilBERT). It uses a weighted rubric across six usability and interoperability criteria and deploys the tools in mixed environments to reveal practical trade-offs. The findings show MLflow excels in ease of use and interpretability, Airflow leads in orchestration and ecosystem support, Metaflow offers a superior developer experience for rapid prototyping, and Kubeflow Pipelines targets production-grade Kubernetes deployments with higher setup costs. The results provide actionable guidance for teams based on infrastructure maturity and objectives, and outline concrete directions for extending evaluations to more production-like scenarios.

Abstract

Given the increasing adoption of AI solutions in professional environments, it is necessary for developers to be able to make informed decisions about the current tool landscape. This work empirically evaluates various MLOps (Machine Learning Operations) tools to facilitate the management of the ML model lifecycle: MLflow, Metaflow, Apache Airflow, and Kubeflow Pipelines. The tools are evaluated by assessing the criteria of Ease of installation, Configuration flexibility, Interoperability, Code instrumentation complexity, result interpretability, and Documentation when implementing two common ML scenarios: Digit classifier with MNIST and Sentiment classifier with IMDB and BERT. The evaluation is completed by providing weighted results that lead to practical conclusions on which tools are best suited for different scenarios.
Paper Structure (27 sections, 1 figure, 2 tables)

This paper contains 27 sections, 1 figure, 2 tables.

Table of Contents

  1. Introduction
  2. MLOps frameworks
  3. MLflow
  4. Metaflow
  5. Apache Airflow
  6. Kubeflow Pipelines
  7. Experimental setup
  8. Assessed features
  9. Use cases
  10. Digit classifier with MNIST
  11. Sentiment classifier with IMDB and BERT
  12. Deployment
  13. Results
  14. Ease of Installation
  15. Configuration flexibility
  16. ...and 12 more sections

Figures (1)

  • Figure 1: MLOps deployment architecture illustrating the evaluation workflow. Code changes in the repository trigger GitHub Actions jobs (MLflow_job, Metaflow_job, Airflow_job, Kubeflow_job) that deploy to respective environments: Metaflow Sandbox for prototyping, MLflow and Apache Airflow servers on DigitalOcean droplets, and Kubeflow Pipelines on a local Minikube cluster.