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Machine Learningbeginner

Network Security

In this comprehensive project, you will step into the shoes of a Senior ML Engineer. You won't just train a model; you will architect a robust, scalable system to detect network security threats. You will abandon ad-hoc scripting and adopt professional workflows, starting with a structured development environment and rigorous Data Engineering (ETL) pipelines. You will implement a complete MLOps workflow. This includes tracking experiments with MLflow and Dagshub to ensure reproducibility. You will effectively manage data using MongoDB Atlas and automate the flow of data using ingestion and transformation pipelines.

24 lectures

What You Will Learn

Mastering the setup of a professional data science project structure and environment.
Implementing ETL pipelines for efficient data management.
Building automated data ingestion systems using Python.
Applying data validation and transformation techniques for data quality.
Utilizing MLflow for experiment tracking and model versioning.
Implementing CI/CD pipelines for automated model deployment.
Deploying machine learning models to AWS cloud infrastructure.

System Architecture

Network Security Architecture Diagram

High-level architecture overview of the Network Security .

What You'll Build

  • A modular data ingestion system with configuration management.
  • Automated data validation and transformation pipelines.
  • An MLflow-integrated model training and tracking system.
  • A Dockerized application with CI/CD deployment to AWS EC2.
  • A complete ETL pipeline from data extraction to model deployment.
Network Security
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