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Make or buy strategy for Machine Learning Operations - MLOps.

Diego Nogare1,2, Ismar F Silveira1, Renato Banzai2

  • 1Universidade Presbiteriana Mackenzie, Programa de Pós-Graduação em Engenharia Elétrica e Computação - PPGEEC, Rua da Consolação, 930, 01302-907 São Paulo, SP, Brazil.

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Organizations must weigh cost, expertise, and strategic alignment when deciding whether to build or buy Machine Learning Operations (MLOps) solutions. This research guides MLOps strategy, analyzing tools for effective machine learning model lifecycle management.

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Area of Science:

  • Computer Science
  • Artificial Intelligence
  • Business Strategy

Background:

  • Organizations face complex decisions regarding Machine Learning Operations (MLOps) implementation.
  • The 'make or buy' strategy for MLOps requires balancing cost, quality, expertise, and strategic goals.

Purpose of the Study:

  • To analyze the make or buy strategy for MLOps solutions.
  • To provide a guide for organizations in deciding between internal development and external procurement of MLOps capabilities.
  • To assess various MLOps tools for machine learning model lifecycle management.

Main Methods:

  • Qualitative and quantitative reviews of MLOps tools.
  • Analysis of factors including cost, quality, technical expertise, and strategic alignment.
  • Exploration of product complexity, core competencies, and risk management in MLOps.

Main Results:

  • A framework for evaluating MLOps 'make or buy' decisions is presented.
  • Comparative reviews of popular MLOps platforms like MLFlow, Airflow, Kubeflow, Databricks, Dataiku, H2O, AWS, Azure, and GCP are provided.
  • The importance of project-specific needs in tool selection is emphasized.

Conclusions:

  • Effective MLOps implementation hinges on a well-defined strategy and appropriate tool selection.
  • Understanding organizational competencies and project requirements is crucial for successful MLOps adoption.
  • The research offers insights into navigating the MLOps landscape for competitive advantage.