Building Custom Transformers in PySpark for Smarter ML Pipelines

When working with big data, PySpark is commonly used for data preprocessing and transformation at scale. While built-in PySpark ML transformers like StandardScaler or StringIndexer handle many common tasks, real-world use cases often require custom transformations tailored to your data and domain.

That’s where custom PySpark ML transformers come in, allowing you to embed custom logic directly into your ML workflows, keeping them clean, reusable, and production-ready.

In our latest blog, we walk you through:

 How Spark ML Pipelines work using concepts like transformers, estimators, and parameters
A simple example to illustrate the concept: ValueRounder, a custom transformer that rounds numerical values
How to plug custom logic directly into your ML pipeline
Tips for building and working with PySpark ML Pipeline components

If you’re exploring how to scale your ML workflows or design smarter pipelines using tools like PySpark  this post is a great place to start.

📖 Read the full blog on Medium and explore what’s possible when flexibility meets structure in ML with PySpark.