Make or buy strategy for Machine Learning Operations - MLOps.

Abstract

This research addresses the make or buy strategy for Machine Learning Operations (MLOps), exploring the decision between developing internally or purchasing computational solutions for Machine Learning projects. Considering factors such as cost, quality, technical expertise and strategic alignment, organizations face the challenge of balancing product complexity, core competencies and risk management. This research highlights the importance of understanding the needs of each project when analyzing existing offers to solve problems and maintain competitiveness in the market, offering a guide for drive and support your decision. Additionally, qualitative and quantitative reviews of MLFlow, Airflow, Kubeflow, Databricks, Dataiku, H2O, Amazon AWS, Microsoft Azure, and Google GCP tools are presented, which facilitate the life-cycle management of machine learning models. This research contributes to the understanding of the challenges and strategies involved in the effective implementation of MLOps projects.