️ Data Engineering & MLOps Fundamentals
🛠️ Data Engineering & MLOps Fundamentals
Building production-grade data pipelines requires more than just scripting; it requires rigorous validation, scalability, and memory efficiency.
🔍 Section Overview
Transition from notebooks to production-grade data engineering.
1. ETL Pipelines & MLOps
Explore memory-efficient ETL patterns using Generators, schema validation with Pydantic, and parallel processing techniques.
🎯 Key Learning Goals
- Build pipelines that can handle datasets larger than memory.
- Implement strict schema validation for data integrity.
- Scale processing using parallel and distributed patterns.