 added as your pipeline evolves in MLOps maturity.Writing custom component flavors

You can take control of how ZenML behaves by creating your own components. This is done by writing custom component flavors. To learn more, head over to the general guide on writing component flavors, or read more specialized guides for specific component types (e.g. the custom orchestrator guide).

Integrations

Categorizing the MLOps stack is a good way to write abstractions for an MLOps pipeline and standardize your processes. But ZenML goes further and also provides concrete implementations of these categories by integrating with various 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.

Airflow or

Kubeflow, track experiments using

MLflow Tracking or

Weights & Biases, and transition seamlessly from a local

MLflow deployment to a deployed model on Kubernetes using

Seldon Core.

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.

Available integrations

We have a dedicated webpage that indexes all supported ZenML integrations and their categories.

Another easy way of seeing a list of integrations is to see the list of directories in the integrations directory on our GitHub.

Installing ZenML integrations

Before you can use integrations, you first need to install them using zenml integration install, e.g., you can install Kubeflow, MLflow Tracking, and Seldon Core, using:

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: