"Prefect 2 Workflows: Python-Native Orchestration for Data and ML Pipelines" In the era of modern data engineering, static DAGs are no longer sufficient for complex, dynamic workloads. This book is the definitive guide for experienced data engineers, machine learning practitioners, and advanced Python developers looking to master code-first orchestration. By embracing Prefect’s philosophy of eliminating "negative engineering," this resource empowers you to transform standard Python scripts into highly observable, resilient, and scalable production pipelines without sacrificing or rewriting your underlying business logic. Through deeply technical explorations of Prefect’s core architecture, you will learn to harness decorators to build robust flows and granular tasks equipped with configurable retries, caching, and dynamic parallelism. The book guides you through the complete workflow lifecycle, from local debugging and granular state management to deploying containerized code via work pools, Docker, and Kubernetes. You will master advanced execution models, seamlessly integrating distributed task runners like Dask and Ray to tackle demanding ML training and large-scale data transformations. Going beyond basic tutorials, this comprehensive resource zeroes in on real-world production patterns, detailing modular configuration with Blocks, secure secret management, and event-driven automation. Designed for readers with a solid foundation in Python, the text avoids oversimplification to provide actionable, in-depth architectural strategies. Whether you are coordinating comple
