The post-development phase of a machine learning project is where the focus shifts from initial development and deployment to ongoing maintenance, optimization, and evaluation. Through an equity-focused lens, this section will cover the post-development stages of testing, model deployment and monitoring, model explanation, model retraining, output adjustment and interpretation, and maintenance.

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Developers wishing to dive deeper into the technical aspects of ensuring equity in AI can access our GitHub site.