Lilly Tech Systems
Learn ETL for Machine Learning
Master the art of building robust data pipelines — from extracting raw data from diverse sources, through transformation and feature engineering, to loading production-ready datasets for ML training and inference.
Your Learning Path
Follow these lessons in order, or jump to any topic that interests you.
1. Introduction
What is ETL for ML? Why data pipelines are the backbone of every successful machine learning project.
2. Data Extraction
Extract data from APIs, databases, files, streams, and web scraping for ML pipelines.
3. Transformation
Data cleaning, feature engineering, normalization, encoding, and handling missing values for ML.
4. Loading
Load transformed data into feature stores, data warehouses, and ML-ready formats for training.
5. Airflow
Orchestrate ETL pipelines with Apache Airflow: DAGs, operators, scheduling, and monitoring.
6. Best Practices
Production-grade ETL patterns, testing strategies, monitoring, and scaling ML data pipelines.
What You'll Learn
By the end of this course, you'll be able to:
Design ETL Pipelines
Architect end-to-end data pipelines that reliably feed high-quality data to your ML models.
Engineer Features
Transform raw data into powerful features using cleaning, encoding, and normalization techniques.
Orchestrate with Airflow
Build automated, scheduled pipelines using Apache Airflow DAGs with proper error handling.
Scale for Production
Apply best practices for testing, monitoring, and scaling ETL pipelines in production environments.