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Bringing a machine learning model into the real world involves a lot more than just modeling. This Specialization will teach you how to navigate various deployment scenarios and use data more effectively to train your model. In this final course, you鈥檒l explore four different scenarios you鈥檒l encounter when deploying models. You鈥檒l be introduced to TensorFlow Serving, a technology that lets you do inference over the web. You鈥檒l move on to TensorFlow Hub, a repository of models that you can use for transfer learning. Then you鈥檒l use TensorBoard to evaluate and understand how your models work, as well as share your model metadata with others. Finally, you鈥檒l explore federated learning and how you can retrain deployed models with user data while maintaining data privacy. This Specialization builds upon our TensorFlow in Practice Specialization. If you are new to TensorFlow, we recommend that you take the TensorFlow in Practice Specialization first. To develop a deeper, foundational understanding of how neural networks work, we recommend that you take the Deep Learning Specialization.