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How to manage machine learning models

In today’s fast-paced analytics development environments, data scientists are often tasked with far more than building a machine learning model and deploying it into production. Now they’re charged with regularly monitoring, fine-tuning, updating, retraining, replacing, and jump-starting models—and in some cases, hundreds or even thousands of models collectively.

As a result, different levels of model management have emerged. In the following, I try to highlight each, from single model management all the way through building an entire model factory.

Machine learning workflow basics

You may be wondering, how do I use the result of my training procedure to score new incoming data? There are a lot of options, such as scoring within the same system that was used for training and exporting models in standardized formats. Alternatively, you can push models into other systems, like scoring models as SQL statements within a database or containerizing models for processing in an entirely different runtime environment. From the model management perspective, you just need to be able to support all required options.

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