Batch Model Pipelines with Cloud Dataflow and Apache Beam

As datasets grow, running machine learning models on millions of rows requires robust, distributed infrastructure. Scaling these workloads manually is complex, but using dedicated pipeline frameworks simplifies the process. In this text-only course, you will learn how to design, execute, and monitor batch processing pipelines for machine learning inference. You will start with the fundamental concepts of distributed data processing, progress to writing pipeline logic, and finish by deploying production-ready workflows that store predictions efficiently. What you'll learn: - Understand the core architecture of Cloud Dataflow and Apache Beam for batch processing - Write robust pipeline transforms to ingest, preprocess, and format large-scale datasets - Integrate machine learning models directly into pipeline steps for scalable batch inference - Configure BigQuery destinations to store, query, and manage model predictions - Test pipeline logic locally using modern unit testing practices and pytest - Monitor pipeline execution, optimize resource allocation, and debug common bottlenecks You will start with essential definitions and architecture basics before moving on to hands-on pipeline construction. The course guides you through reading and understanding local pipelines, scaling them to the cloud, and applying testing and monitoring best practices through written explanations and code examples. This course is designed for beginner data engineers, developers, and aspiring machine learning practitioners who want to learn scalable data processing. No prior experience with Cloud Dataflow or Apache Beam is required. Start reading today to build efficient, scalable pipelines for your machine learning workflows.