Machine learning operations (MLOps) canvas for data products

What is MLOps, and why is it important?

MLOps is a set of practices and tools that aims to make productionizing, deploying, and maintaining machine learning models in production reliable and efficient.

  1. The time to market ML products is unsatisfying
  2. ML products often break and teams are reluctant to update them
  3. You often encounter problems that you’ve not anticipated when moving ML to production or supporting it

What is a machine learning operations canvas?

A machine learning operations (MLOps) canvas is a template used for describing and visualizing production systems with machine learning. It covers a wide range of aspects that have to be taken into account to sustainably run machine learning applications in production.

Download Xomnia’s MLOps canvas

We have created a canvas focused on machine learning operations. Although every aspect of a machine learning project deserves a separate document to describe it, it makes sense to focus on details related to running ML in the production stage.

About the authors

This canvas is created by Xomnia’s “Architects”, a group of machine learning experts and analytics translators led by Andrey Kudryavets, lead data engineer at Xomnia. Members of the group have strong backgrounds in strategic management, MLOps, software development, and data science, as well as years of experience in bringing and supporting machine learning systems in production for a broad range of clients (government; healthcare; defense, safety, and security; education; retail; and aviation). The canvas is the quintessence of practical experience and “hard” learnings of the group.



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