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.

For example, you can orchestrate your ML pipeline workflows using 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.

ZenML is the glue

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 dependencies for integrations and stacks

ZenML provides a way to export the package requirements for both individual integrations and entire stacks, enabling you to install the necessary dependencies manually. This approach gives you full control over the versions and the installation process.

Exporting integration requirements

You can export the requirements for a specific integration using the zenml integration export-requirements command. To write the requirements to a file and install them via pip, run:

zenml integration export-requirements <INTEGRATION_NAME> --output-file integration_requirements.txt
pip install -r integration_requirements.txt

If you prefer to see the requirements without writing them to a file, omit the --output-file flag:

zenml integration export-requirements <INTEGRATION_NAME>

This will print the list of dependencies to the console, which you can then pipe to pip:

zenml integration export-requirements <INTEGRATION_NAME> | xargs pip install

Exporting stack requirements

To install all dependencies for a specific ZenML stack at once, you can export your stack's requirements:

zenml stack export-requirements <STACK_NAME> --output-file stack_requirements.txt
pip install -r stack_requirements.txt

Omitting --output-file will print the requirements to the console:

zenml stack export-requirements <STACK_NAME>

You can also pipe the output directly to pip:

zenml stack export-requirements <STACK_NAME> | xargs pip install

If you use a different package manager such as uv, you can install the exported requirements by replacing pip install -r … with your package manager's equivalent command.

Help us with integrations!

There are countless tools in the ML / MLOps field. We have made an initial prioritization of which tools to support with integrations that are visible on our public roadmap.

We also welcome community contributions. Check our Contribution Guide and External Integration Guide for more details on how to best contribute to new integrations.