Comment on page
Overview of third-party ZenML integrations
Categorizing the MLOps stack is a good way to write abstractions for a MLOps pipeline and standardize your processes. But ZenML goes further and also provides concrete implementations of these categories by integrating with many different tools for each category. Once code is organized into a ZenML pipeline, you can supercharge your ML workflows with the best-in-class solutions from various MLOps areas.
There are lots of moving parts for all the MLOps tooling and infrastructure you require for ML in production and ZenML brings them all together and enables you to manage them in one place. This also allows you to delay the decision of which MLOps tool to use in your stack as you have no vendor lock-in with ZenML and can easily switch out tools as soon as your requirements change.
ZenML is the glue
zenml integration install kubeflow mlflow seldon -y
Under the hood, this simply installs the preferred versions of all integrations using pip, i.e., it executes in a sub-process call:
pip install kubeflow==<PREFERRED_VERSION> mlflow==<PREFERRED_VERSION> seldon==<PREFERRED_VERSION>
-yflag confirms all
pip installcommands without asking you for confirmation for every package first.
You can run
zenml integration --helpto see a full list of CLI commands that ZenML provides for interacting with integrations.
Note, that you can also install your dependencies directly, but please note that there is no guarantee that ZenML internals with work with any arbitrary version of any external library.
Last modified 1mo ago