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Putting ML models into production

You know several best practices when considering putting models into production in your MLOps lifecycle. You can deploy automation tools to put models into the hands of your users faster. You can maximize model scalability and efficiency by effectively packaging environments and models.

To be the most effective, you will need to recall these best practices and know how specifically they are helping your ML pipeline. It's time to put this knowledge to the test!

This exercise is part of the course

Developing Machine Learning Models for Production

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