Advanced Deployment Scenarios with TensorFlow (Coursera)

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.

Course 4 of 4 in the TensorFlow: Data and Deployment Specialization.
What You Will Learn

- Use TensorFlow Serving to do inference over the web

- Navigate TensorFlow Hub, a repository of models that you can use for transfer learning

- Evaluate how your models work and share model metadata using TensorBoard

- Explore federated learning and how to retrain deployed models while maintaining data privacy

Syllabus

WEEK 1: TensorFlow Extended

WEEK 2: Sharing pre-trained models with TensorFlow Hub

WEEK 3: Tensorboard: tools for model training

WEEK 4: Federated Learning