Training and Deployment with TensorFlow

This unit expands foundational TensorFlow concepts into hands-on model training, evaluation, and deployment workflows. Students will work with data pipelines, graphs, estimators, device/cluster configuration, and cloud tools to prepare reproducible, production-ready applications.

Learning Objectives

• Analyze and preprocess input data by standardizing values, creating tensors (including random tensors), and constructing iterators to feed models for training and evaluation
• Apply TensorFlow graph constructs, operations, layers, and proper weight initialization to build reproducible models and execute polynomial regression experiments while writing and interpreting log messages
• Demonstrate training and testing of Estimators by configuring devices, executing on local and cluster environments, and interpreting evaluation metrics to validate model performance
• Configure GCP projects and the Cloud SDK to run and deploy TensorFlow applications, execute first applications, and apply coding style and operational practices for maintainable production workflows