Introduction
Prefect is a modern, Python-native workflow orchestration framework that enables teams to define, schedule, monitor, and scale workflows with ease—whether in development, staging, or production environments. Prefect allows engineers to build workflows as code using a declarative and expressive API, while handling execution robustness through retries, caching, dependency management, and state tracking. It is particularly well-suited for orchestrating data pipelines, ML training jobs, evaluations, and retrieval updates, especially when reliability and observability are key.
Key benefits of using Prefect include:
Python-First Workflow Definitions: Workflows (called Flows) and tasks are written as native Python functions, enabling full use of the language’s ecosystem and dev tooling.
Dynamic and Reactive Execution: Supports branching logic, conditional execution, retries, timeouts, and concurrency controls—making workflows flexible and fault-tolerant.
Built-In Observability and Logging: Provides a real-time dashboard (Prefect Cloud or Prefect Server) with full visibility into task states, run history, logs, and failure diagnostics.
Hybrid Execution Architecture: Supports execution in multiple environments—including local dev, Kubernetes, Docker, and cloud runners—while managing orchestration centrally.
Seamless Scheduling and Notifications: Offers built-in scheduling, parameterization, and alerting to monitor flows and trigger downstream systems reliably.
Prefect is used to orchestrate workflows such as data ingestion, document processing, embedding generation, model evaluation, RAG index updates, and periodic monitoring checks. It integrates easily with dbt, MLflow, Hugging Face, and storage systems, making it a flexible choice for coordinating cross-cutting AI and data systems. By adopting Prefect, you can ensure its data and AI workflows are resilient, observable, and easy to iterate on—empowering teams to focus on delivering value, not managing failures.