End to End MLOPS Data Science Project Implementation With Deployment

Implement an end-to-end MLOps data science project, from initial setup to deployment on AWS EC2 using GitHub Actions. Learn to create a GitHub repository, structure the project using templates, set up logging, perform data ingestion, validation, and transformation, train models, build prediction pipelines, and deploy the solution. Gain hands-on experience with MLflow for experiment tracking and model management throughout the development process.

Introduction to Project
Create a Repository In Github Account
Create Structure Using Template.py
Implementing Setup.py
Logging Implementation
Data Ingestion
Data Validation
Data Transformation
Model Trainer
Prediction Pipeline
Deployment In EC2 with app runner