In order to recap the Machine Learning with TensorFlow on Google Cloud Platform learning path, this section starts with a workshop in which you will do End-to-End Machine Learning with TensorFlow on Google Cloud Platform. Then you learn about the components and best practices of a high-performing ML system in production environments.

This section offers a look at different strategies for building an image classifier using convolutional neural networks. You will improve a machine learning model's accuracy with augmentation, feature extraction, and fine-tuning hyperparameters while trying to avoid overfitting the data. You will also look at practical issues that arise, for example, when you don’t have enough data; as well as how to incorporate the latest research findings into our models. You will get hands-on practice building and optimizing your own image classification models on a variety of public datasets. The section next introduces sequence models and their applications, including an overview of sequence model architectures and how to handle inputs of variable length. You will get hands-on practice building and optimizing your own text classification and sequence models on a variety of public datasets.

In this section, you'll apply your knowledge of classification models and embeddings to build a ML pipeline that functions as a recommendation engine.