Machine Learning Engineering for Production (MLOps) VS TensorFlow: Data and Deployment

I’ve read the description of both courses but I couldn’t decide which one to take; their intro look similar to me.
Which of the two classes do you suggest or think is better.

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It depends what you want to learn.

The data and deployment is related to deployment of models in mediums after the model has been built.

The MLOps is more about the creation of entire pipeline from feature engineering to model building, deployment and recursive update of the pipeline with time.

I did the first course of both and found the MLOps a bit more interesting from my perspective but both courses teach you a set of different things so you can not really compare head to head.

Thank you for clarification.