Ementorhub

What you'll 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

Skills you'll gain

Data Visualization

Artificial Neural Networks

Deep Learning

Information Privacy

Web Servers

Tensorflow

Application Deployment

Applied Machine Learning

Data Security

MLOps (Machine Learning Operations)

There are 4 modules in this course

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.

Sharing pre-trained models with TensorFlow Hub

Tensorboard: tools for model training

Federated Learning