PySpark Model Pipelines for Cloud Data Lakes

As data volumes grow, deploying machine learning models requires scalable infrastructure that can handle massive datasets without breaking. Transitioning from local prototypes to cloud-based batch pipelines is a critical step for modern data professionals. This text-only course guides you through the foundational concepts and practical steps of building scalable batch model pipelines using PySpark. You will learn how to process large-scale data, integrate with cloud data lakes, and set up scheduled predictions. What you'll learn: - Understand the core terminology of distributed computing and PySpark architecture - Configure PySpark data pipelines to read from and write to cloud data lakes - Build robust batch processing pipelines for machine learning model inference - Apply scheduling concepts to automate model predictions at regular intervals - Implement basic data validation and quality checks within your cloud pipelines - Deploy pipelines efficiently across AWS and GCP environments This course begins with essential definitions of distributed systems and data lakes, then progresses to hands-on PySpark configuration, cloud storage integration, and automated batch scheduling. It is designed for aspiring data scientists, data engineers, and analysts who are new to cloud-scale data processing and PySpark, with no advanced cloud experience required. Start reading today to master the tools that power modern, scalable data pipelines in the cloud.