Full-Stack Deep Learning with Python

Introduction

• Full-stack deep learning, MLOps, and MLflow
• Prerequisites

1. An Overview of Full-Stack Deep Learning

• Introducing full-stack deep learning
• Introducing MLOps
• Introducing MLflow
• Setting up the environment on Google Colab
• Running MLflow and using ngrok to access the MLflow UI

2. Model Training and Evaluation Using MLflow

• Loading and exploring the EMNIST dataset
• Logging metrics, parameters, and artifacts in MLflow
• Set up the dataset and data loader
• Configuring the image classification DNN model
• Training a model within an MLflow run
• Exploring parameters and metrics in MLflow
• Making predictions using MLflow artifacts

3. Model Training and Hyperparameter Tuning

• Preparing data for image classification using CNN
• Configuring and training the model using MLflow runs
• Visualizing charts, metrics, and parameters on MLflow
• Setting up the objective function for hyperparameter tuning
• Hyperparameter optimization with Hyperopt and MLflow
• Identifying the best model
• Registering a model with the MLflow registry

4. Model Deployment and Predictions

• Setting up MLflow on the local machine
• Workaround to get model artifacts on the local machine
• Deploying and serving the model locally

Conclusion

• Summary and next steps