Full-Stack Deep Learning with Python
Offered By: LinkedIn Learning
Course Description
Overview
Increase your knowledge and get a hands-on understanding of full-stack deep learning with Python.
Syllabus
Introduction
- Full-stack deep learning, MLOps, and MLflow
- Prerequisites
- 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
- 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
- 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
- Setting up MLflow on the local machine
- Workaround to get model artifacts on the local machine
- Deploying and serving the model locally
- Summary and next steps
Taught by
Janani Ravi
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