Lilly Tech Systems
Apache Spark for ML
Master distributed machine learning with Apache Spark — from PySpark fundamentals and MLlib to feature engineering and production-grade pipelines.
Your Learning Path
Follow these lessons in order, or jump to any topic that interests you.
1. Introduction
What is Apache Spark? Distributed computing fundamentals, Spark architecture, and the ML ecosystem.
2. PySpark Basics
SparkSession, RDDs, DataFrames, transformations, actions, and SQL queries with PySpark.
3. MLlib
Spark's machine learning library: classification, regression, clustering, and recommendation algorithms.
4. Feature Engineering
Transformers, estimators, vectorization, encoding, scaling, and feature selection at scale.
5. Pipelines
Build end-to-end ML pipelines with stages, cross-validation, hyperparameter tuning, and persistence.
6. Best Practices
Performance tuning, memory management, cluster sizing, monitoring, and production deployment tips.
What You'll Learn
By the end of this course, you'll be able to:
Distributed ML
Train machine learning models on massive datasets using Spark's distributed computing engine.
PySpark Fluency
Write efficient PySpark code for data manipulation, feature engineering, and model training.
MLlib Mastery
Use Spark MLlib for classification, regression, clustering, and recommendation at scale.
Production Pipelines
Build robust, reusable ML pipelines with cross-validation, tuning, and model persistence.