Define pipelines using an intuitive and Pythonic syntax
Turn your existing Python functions into ZenML steps via a single line of code.
Write standard Python code to connect your steps into pipelines and execute them.
Develop code how you want - no need to adapt your code to complex coding paradigms.
Supercharge your ML workflows with powerful pipelining features
Automatically save, version, and cache your datasets and models for better reproducibility and less compute costs.
Make your code run on any infrastructure - locally, on-prem, or in the cloud.
Automate your workflows with powerful production orchestrators without changing your code.
Develop and run your ML workflows in any environment
ZenML pipelines run on any infrastructure, so your code can be seamlessly transitioned to production, no matter where you have developed it.
Develop code wherever you want - on your local machine or in a server, in Python scripts or in Jupyter notebooks, it’s all up to you.
ZenML doesn’t require any special infrastructure or software to run, so there is no need to wait for environments to spin up.
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.
Matt Squire
CTO at Fuzzy Labs
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