Applying the MLOps Lifecycle

4 years ago towardsdatascience.com

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It is a new field and most teams tasked with MLOps projects are currently coming at it from a different background.

What is needed is to understand the specific needs of each MLOps project.

A common approach to deployment is to use an off-the-shelf serving solution that can work for the framework.

For platform-level tools to offer insights about data going through models, it is important to know what type of data is in play.

Data can be unpredictable and that can mean we have to be cautious about rollouts and that we have to monitor at the level of data and not just errors.

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