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.
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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
