Integrations
Use these tools out-of-the-box with ZenML.
ZenML integrates with many different third-party tools as implementations for many different ZenML abstractions.
Once code is organized into a ZenML pipeline, you can supercharge your ML development with powerful integrations on multiple MLOps stacks. There are lots of moving parts for all the MLOps tooling and infrastructure you require for ML in production and ZenML aims to bring it all together under one roof.
For example, we currently support Airflow and Kubeflow as third-party orchestrators for your ML pipeline code. Experiment trackers like MLflow Tracking and Weights & Biases can easily be added to your ZenML pipeline. And you can seamlessly transition from a local MLflow deployment to a deployed model on Kubernetes using Seldon Core.
All of this allows you to write your code now and add the right tool for the job as soon as the need arises.
ZenML is the glue
These are the third-party integrations that ZenML currently supports:
Integration
Status
Type
Implementation Notes
Example
Apache Airflow
Orchestrator
Works for local environment.
Apache Beam
Distributed Processing
AWS
Container Registry
Use the AWS container registry to store your containers.
AWS
Secrets Manager
Use AWS as a secrets manager.
AWS
Step Operator
Sagemaker as a ZenML step operator.
Azure
Artifact Store
Use Azure Blob Storage buckets as ZenML artifact stores.
Azure
Step Operator
Use AzureML as a step operator to supercharge specific steps.
BentoML
Deployment
Looking for community implementors.
Dash
Visualizer
For Pipeline and PipelineRun visualization objects.
lineage
Evidently
Monitoring
Allows for visualization of drift as well as export of a Profile object.
Facets
Visualizer
Quickly visualize your datasets using facets.
Feast
Feature Store
Use Feast with Redis for your online features.
GitHub
Orchestrator
Use GitHub Actions to orchestrate your ZenML pipelines.
GCP
Artifact Store
Use GCS buckets as a ZenML artifact store.
GCP
Step Secrets Manager
Use the GCP Secret Manager.
GCP
Step Operator
Vertex AI as a ZenML step operator.
GCP
Orchestrator
Execute your ZenML pipelines using Vertex AI Pipelines.
Graphviz
Visualizer
For Pipeline and PipelineRun visualization objects.
Great Expectations
Data Validation
Looking for community implementors.
Hugging Face
Materializer
Use Hugging Face tokenizers, datasets and models.
✅ means the integration is already implemented. ⛏ means we are looking to implement the integration soon.

Help us with integrations!

There are many tools in the ML / MLOps field. We have made an initial prioritization of which tools to support with integrations, but we also welcome community contributions. Check our Contributing Guide for more details on how best to contribute.
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