Enterprise ML Platforms Done Right

Many companies are attempting to speed up the delivery of their machine learning (ML) projects by creating platforms. While a few have succeeded, some have experienced significant failures, and most have ended up somewhere in the middle. This can happen when they address MLOps without first addressing their organizational structure and operating model. In this article, we will explore common pitfalls enterprises encounter when building ML platforms and provide solutions to help overcome these obstacles. We will tackle five common pitfalls enterprises face when getting their platform up and running and propose prescriptive solutions for each. To simplify the language, we will use the term "you" to refer to the team responsible for building and maintaining the platform.