Module 4: Deploy
Module Overview
This module focuses on deploying neural networks to production environments. You'll learn how to save and export trained models, convert them to optimized formats for deployment, and serve them using various deployment options. The module covers strategies for model monitoring, maintenance, and scaling to handle real-world workloads. By the end of this module, you'll be able to take your trained neural networks and make them accessible for real-world applications.
Learning Objectives
- Regularization strategies for Deep Learning
- Saving and exporting Tensorflow models
- Writing custom callbacks in Tensorflow/Keras
Guided Project
Open DS_424_Deploy_Lecture.ipynb in the GitHub repository to follow along with the guided project.
Module Assignment
Continue using TensorFlow Keras and the Quickdraw dataset to explore regularization techniques including L1/L2 regularization, dropout, and max norm constraints. Analyze their effects on model performance and learned weights, then practice saving and loading your trained models.
Assignment Solution Video
Additional Resources
Regularization Techniques
- TensorFlow: Keras Regularizers Documentation
- Dropout: A Simple Way to Prevent Neural Networks from Overfitting
Model Saving and Deployment
- TensorFlow: SavedModel Guide
- TensorFlow Serving Documentation
- LiteRT/(formerly TensorFlow Lite) Guide