Model Training, Optimization & Deployment

This unit covers advanced TensorFlow topics for producing, optimizing, debugging, and deploying machine learning models. Students will integrate feature processing, training best practices, estimator APIs, and cloud/cluster deployment to prepare models for real-world use and further study.

Learning Objectives

• Analyze the effects of feature columns, batch normalization, mini-batching, and different optimization algorithms on model convergence and training stability using quantitative metrics
• Apply TensorFlow APIs to construct robust training pipelines by implementing Datasets, Iterators, Feature Columns, variable scope management, Savers, and Estimators to manage variables and checkpoints
• Demonstrate deployment and execution of TensorFlow applications in cloud and distributed environments by configuring clusters, using the Cloud SDK, running Estimators in a cluster, and accessing experiment artifacts
• Evaluate model behavior and debug training processes by inspecting weights and gradients, tuning learning rates and optimizers, and applying improvements to the deep learning pipeline