Embark on a comprehensive 3-hour and 51-minute journey through an end-to-end Deep Learning project, focusing on MLOps tools like DVC and deployment using GitHub Actions on Azure and AWS. Learn to create a Chicken Disease Classification system, starting from project setup and data ingestion to model training, evaluation, and deployment. Explore crucial concepts such as logging, exception handling, and utility modules while implementing various project workflows. Gain hands-on experience with notebook experiments and their final implementations for data ingestion, base model preparation, callbacks, model training, and evaluation. Master the art of writing DVC files for pipeline tracking, creating prediction pipelines, and developing user applications. Conclude with in-depth tutorials on CI/CD deployment processes for both AWS and Azure cloud platforms.
End to End Deep Learning Project Using MLOPS DVC Pipeline With Deployments Azure and AWS - Krish Naik
Krish Naik via YouTube
Overview
Syllabus
- Introduction
- Project Introduction
- Prerequisite
- Problem Statement Chicken Disease
- Project Demo
- Github Repository Setup
- Project Template Creation
- Requirements Installation & Project Setup
- Logging, Exception & Utils Modules
- Project Workflows
- Data Ingestion Notebook Experiment
- Data Ingestion Final Implementation
- Prepare Base Model Notebook Experiment
- Prepare Base Model Final Implementation
- Prepare Callbacks Notebook Experiment
- Prepare Callbacks Final Implementation
- Model Trainer Notebook Experiment
- Model Trainer Final Implementation
- Model Evaluation Notebook Experiment
- Model Evaluation Final Implementation
- Writing DVC file for tracking piplines
- Prediction Pipeline & User App
- Project CI/CD Deployment on AWS
- Project CI/CD Deployment on Azure
Taught by
Krish Naik
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