This post was co-written with Tobias Wenzel, Software Engineering Manager for the Intuit Machine Learning Platform.
We all appreciate the importance of a high-quality and reliable machine learning (ML) model when using autonomous driving or interacting with Alexa, for examples. ML models also play an important role in less obvious ways—they’re used by business applications, healthcare, financial institutions, amazon.com, TurboTax, and more.
As ML-enabled applications become core to many businesses, models need to follow the same vigor and discipline as software applications. An important aspect of MLOps is to deliver a new version of the previously developed ML model in production by using established DevOps practices such as testing, versioning, continuous delivery, and monitoring.
There are several prescriptive guidelines around MLOps, and this post gives an overview of the process that you can follow and which tools to use for testing. This is based on collaborations between Intuit and AWS.

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