Building and Executing TensorFlow Models

This unit teaches students how to assemble, train, test, and run TensorFlow models in both local and cloud/cluster environments. Learners will standardize and prepare data, construct model components (tensors, layers, estimators), and apply best practices for device configuration, logging, and deployment.

Learning Objectives

• Analyze dataset requirements and implement data preprocessing pipelines (iterators, standardization, and random tensor generation) to produce inputs suitable for TensorFlow training workflows
• Apply TensorFlow constructs and best practices (tensors, layers, polynomial regression, weight initialization, graphs, and style conventions) to build and refine models
• Demonstrate the ability to train, test, and instrument estimators and applications—using logging, metrics, and unit tests—and execute them on configured devices, clusters, or local runtimes
• Evaluate options for deploying and managing TensorFlow workloads in cloud environments by configuring GCP projects, using the Cloud SDK, and selecting appropriate machine learning frameworks and operational settings