Advanced Deployment Scenarios with TensorFlow

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Overview

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.

Syllabus

TensorFlow Extended

Sharing pre-trained models with TensorFlow Hub

Tensorboard: tools for model training

Federated Learning

Plataforma