Write Local, Run anywhere
Simple, straightforward syntax
No vendor lock-in
Standardized & reproducible ML
Open, extensible, integrated
ZenML vs Orchestrators
ZenML vs Exp Trackers
ZenML vs e2e Platforms
ZenML OSS vs Cloud
Installation & Setup
ZenML vs Cloud Platforms
More Than a E2E Platform
ZenML vs AWS Sagemaker, GCP Vertex AI, ClearML and more
Understand how ZenML stands apart from traditional e2e platforms
Run the same workloads on any cloud to gain strategic flexibility
ZenML does not tie your work to one cloud.
Define infrastructure as stack components independent of your code
Run any code on any stack with minimum fuss
50+ Integrations with the most popular cloud and open-source tools
From experiment trackers like MLflow and Weights&Biases to model deployers like Seldon and BentoML, ZenML has integrations for tools across the lifecycle.
Flexibly run workflows across all clouds or orchestrations tools such as Airflow or Kubeflow.
AWS, GCP, and Azure integrations all supported out of the box.
Avoid getting locked in to a vendor
Avoid tangling up code with tooling libraries that make it hard to transition.
Easily set up multiple MLOps stacks for different teams with different requirements.
Switch between tools and platforms seamlessly.
"Many teams still struggle with managing models, datasets, code, and monitoring as they deploy ML models into production. ZenML provides a solid toolkit for making that easy in the Python ML world".
Professor of Linguistics and CS at Stanford
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