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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’ll explore four different scenarios you’ll encounter when deploying models. You’ll be introduced to TensorFlow Serving, a technology that lets you do inference over the web. You’ll move on to TensorFlow Hub, a repository of models that you can use for transfer learning. Then you’ll use TensorBoard to evaluate and understand how your models work, as well as share your model metadata with others. Finally, you’ll 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....



Jun 6, 2020

Enjoyed this specialization as much as I did the Tensorflow in practice. Thank you Laurence Moroney and Andrew Ng for getting these cool topics to all of us, so we can contribute back to community.


May 17, 2020

Great work and I highly recommend this course/specialization! Good job of inserting needed edits to update what's happening in real time.


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