MLOps Blog Series Part 1: The art of testing machine learning systems using MLOps

Occasional Contributor
Takuto Higuchi Product Marketing Manager, Data and AI Marketing

Testing is an important exercise in the life cycle of developing a machine learning system to ensure high-quality operations. We use tests to confirm that something functions as it should. Once tests are created, we can run them automatically whenever we make a change to our system and continue to improve them over time. It is a good practice to reward the implementation of tests and identify sources of mistakes as early as possible in the development cycle to prevent rising downstream expenses and lost time.


In this blog, we will look at testing machine learning systems from a Machine Learning Operations (MLOps) perspective and learn about good case practices and a testing framework that you can use to build robust, scalable, and secure machine learning systems. Before we delve into testing, let’s see what MLOps is and its value to developing machine learning systems.




Figure 1: MLOps = DevOps + Machine Learning.


Software development is interdisciplinary and is evolving to facilitate machine learning. MLOps is a process for fusing machine learning with software development by coupling machine learning and DevOps. MLOps aims to build, deploy, and maintain machine learning models in production reliably and efficiently. DevOps drives machine learning operations. Let’s look at how that works in practice.


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