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
Google Cloud
Microsoft Azure

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
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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.
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"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".

Chris Manning
Chris Manning
Professor of Linguistics and CS at Stanford
e2e Platforms Showdown
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