Embark on a comprehensive 4-hour journey to master end-to-end deep learning project implementation for Kidney Disease Classification. Learn to set up a GitHub repository, create project templates, and install necessary requirements. Dive into essential modules like logging, exception handling, and utilities. Explore project workflows, data ingestion, base model preparation, and model training. Integrate MLflow for model evaluation and implement DVC pipelines. Develop prediction pipelines and user applications. Finally, tackle Dockerization and AWS CICD deployment to bring your project to life.
End-to-End Deep Learning Project: Kidney Disease Classification with MLflow, DVC, and Deployment
Krish Naik via YouTube
Overview
Syllabus
- Introduction
- Project Introduction
- Github Repository Setup
- Project Template Creation
- Requirements Installation & Project Setup
- Logging, Exception & Utils Modules
- Project Workflows
- Data Ingestion Component
- Prepare Base Model Component
- Model Trainer Component
- Model Evaluation Component & MLflow Integration
- DVC Pipeline
- Prediction Pipeline & User App
- Dockerization & AWS CICD Deployment
- Conclusion
Taught by
Krish Naik
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