This course teaches learners how to implement end-to-end data science projects with MLflow and deploy them on an AWS EC2 instance using GitHub actions. The course covers creating repositories on GitHub, setting up project structures, implementing logging, data ingestion, validation, transformation, model training, prediction pipelines, and deployment on EC2. The course is designed for individuals interested in mastering MLOps and data science project implementation.
Overview
Syllabus
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
Taught by
Krish Naik
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