Welcome to course 4 of Tensorflow Data and Deployment
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.
Please feel free to search for a topic that you’re interested in or start one of your own if you don’t find what you are looking for!