Model Training, Optimization, Deployment

This unit covers advanced TensorFlow skills for building robust training pipelines, optimizing deep learning models, managing model state, and preparing models for cloud deployment and distributed execution. Students will integrate feature engineering, data pipelines, optimizer strategies, model management, and cloud/cluster configuration to produce reproducible, deployable models.

Learning Objectives

• Analyze model training behavior by inspecting weights, gradients, and experiment logs to identify and quantify sources of underfitting, overfitting, or instability
• Apply TensorFlow input-building techniques (feature columns, Dataset API, Iterator patterns, normalization, and small-random-batch training) to construct efficient, reproducible training pipelines
• Demonstrate model management and reproducibility by configuring variable scope, using Savers and Estimators, selecting and tuning optimizers (including setting learning rates and trying alternative algorithms), and saving checkpoints for deployment
• Evaluate and configure cloud and distributed execution environments by using the Cloud SDK, setting up cluster configurations, running Estimators in a cluster, and accessing experiment artifacts for deployment