Simplify Your MLOps Pipelines
Write simple python locally and scale to your production MLOps stack with one command
Get started quickly
ZenML is completely free and open-source. See the magic with just two simple commands:
$pip install zenml
$zenml go
Author Your Pipeline
ZenML provides an easy-to-use interface to create tool and infrastructure agnostic pipelines for batch processing, continuous training, and deployment. Quickly build, execute and interact with completed runs through ZenML.
HOW IT WORKS
PROBLEM SOLVING
Why our users love ZenML
Chris Manning
@chrmanning
Many, 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
Goku Mohandas
@GokuMohandas
ZenML allows you to quickly and responsibly go from POC to production ML systems while enabling reproducibility, flexibitiliy, and above all, sanity.
Founder of MadeWithML
Richard Socher
@RichardSocher
ZenML allows orchestrating ML pipelines independent of any infrastructure or tooling choices. ML teams can free their minds of tooling FOMO from the fast-moving MLOps space, with the simple and extensible ZenML interface. No more vendor lock-in, or massive switching costs!
Chief Scientist Salesforce and Founder of You.com
Matt Squire
@FuzzyLabsAI
A lot of our teams struggle to bring sanity to their model training processes. ZenML is built in a way that encourages good, maintainable pipelines. It makes it easy to coordinate all the various tools, systems and assets that go into training ML models and, crucially, it gives you as much control over the process as you need.
CTO at Fuzzy Labs
Gabriel Martin
@gabrielmbmb_
ZenML allows you to keep your ML pipeline code cloud-agnostic, enabling faster future migrations to another technology stack. The management of the metadata and artifacts generated at each step is seamless, and allows the user to extend the framework if needed without much effort.
Machine Learning Engineer at Frontiers
