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Model formats

Once your model training pipeline is successfully executed, you need to save your model in a format suitable for storage and deployment within the ML serving application.

In this chapter, you heard about two common formats for this purpose.

For your use case, you realized that you want to be able to train your model using one programming language, and then load it serve it using an entirely different one.

Which format will give you this type of flexibility?

This exercise is part of the course

MLOps Deployment and Life Cycling

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