TensorFlow Training and Operations

This unit covers configuring TensorFlow runtimes and devices, building data pipelines, training and testing estimators and neural network models, and executing applications locally and on Google Cloud. Students apply preprocessing, logging, weight-initialization, and operational tools to run models in single-node and distributed environments, preparing them for production-scale workflows in the next unit.

Learning Objectives

• Configure TensorFlow runtime environments and devices (CPU, GPU, cluster) and set up Google Cloud Project and Cloud SDK settings to enable local and cloud execution
• Implement data preprocessing and input pipelines using iterators, standardized transformations, and randomized tensor generation to prepare datasets for training
• Train, test, and evaluate estimators and neural network models—including polynomial regression and layered architectures—applying proper weight initialization and logging training metrics
• Deploy and execute TensorFlow applications and operations in local, cluster, and GCP environments and analyze operational logs and execution performance