Model Optimization, Deployment, and Scaling

This unit covers advanced TensorFlow techniques for improving model training, managing experiments, and preparing models for production deployment. Students will learn to build scalable data pipelines, tune optimizers and learning rates, manage variables and checkpoints, and run estimators in cloud and cluster environments.

Learning Objectives

• Apply TensorFlow feature columns, dataset pipelines, normalization, and iterators to preprocess and batch data for scalable training and deployment
• Implement and compare optimization strategies—including different optimizers, learning rate schedules, and mini-batch training—to improve convergence and analyze weights and gradients
• Configure and manage variable scope, estimators, savers, checkpoints, and experiment tracking to ensure reproducible training and model persistence
• Deploy and execute trained models in distributed environments by configuring cloud clusters and using the Cloud SDK to run estimators and demonstrate end-to-end deployment readiness