TensorFlow: Data and Deployment Specialization

You know how to build models in TensorFlow, but how do you get it into the hands of real people? This second course teaches you how to run your machine learning models in mobile applications. You’ll learn how to prepare models for a lower-powered, battery-operated devices, then execute models on both Android and iOS platforms. Finally, you’ll explore how to deploy on embedded systems using TensorFlow on Raspberry Pi and microcontrollers.

In this third course, you will: Perform efficient ETL tasks using Tensorflow Data Services APIs; Construct train/validation/test splits of any dataset – either custom or present in TensorFlow Hub Dataset library – using Splits API; Use different modules and functions of the TFDS API to prepare your data for training pipelines; Identify bottlenecks in your input pipelines and increase your workflow efficiency by input parallelization.

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.