airflow

In this Prefect vs Airflow vs ZenML article, we explain the difference between the three platforms and educate you about using them in tandem.

In this Flyte vs Airflow vs ZenML article, we explain the difference between the three platforms and educate you about using them in tandem.

Discover how ZenML's Service Connectors solve one of MLOps' most frustrating challenges: credential management. This deep dive explores how Service Connectors eliminate security risks and save engineer time by providing a unified authentication layer across cloud providers (AWS, GCP, Azure). Learn how this approach improves developer experience with reduced boilerplate, enforces security best practices with short-lived tokens, and enables true multi-cloud ML workflows without credential headaches. Compare ZenML's solution with alternatives from Kubeflow, Airflow, and cloud-native platforms to understand why proper credential abstraction is the unsung hero of efficient MLOps.

8 practical alternatives to Kubeflow that address its common challenges of complexity and operational overhead. From Argo Workflows' lightweight Kubernetes approach to ZenML's developer-friendly experience, we analyze each tool's strengths across infrastructure needs, developer experience, and ML-specific capabilities—helping you find the right orchestration solution that removes barriers rather than creating them for your ML workflows.

We compare ZenML with Apache Airflow, the popular data engineering pipeline tool. For machine learning workflows, using Airflow with ZenML will give you a more comprehensive solution.

Cloud Composer (Airflow) vs Vertex AI (Kubeflow): How to choose the right orchestration service on GCP based on your requirements and internal resources.