Integrations
Use these tools out-of-the-box with ZenML.
ZenML integrates with many different third-party tools.
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.
We currently support Airflow and Kubeflow as third-party orchestrators for your ML pipeline code. ZenML steps can be built from any of the other tools you usually use in your ML workflows, from scikit-learn to PyTorch or TensorFlow.
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
BentoML
Cloud
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
GCP
Cloud
Graphviz
Visualizer
For Pipeline and PipelineRun visualization objects.
Great Expectations
Data Validation
Looking for community implementors
KServe
Inference
Looking for community implementors
Kubeflow
Orchestrator
Either full Kubeflow or Kubeflow Pipelines. Works for local environments currently.
kubeflow
MLFlow
Orchestrator
Looking for community implementors
MLFlow Tracking
Monitoring
Tracks your pipelines and your training runs.
mlflow
numpy
Exploration
pandas
Exploration
Plotly
Visualizer
For Pipeline and PipelineRun visualization objects.
lineage
PyTorch
Training
PyTorch Lightning
Training
scikit-learn
Training
Seldon
Cloud
Looking for community implementors
Tensorflow
Training
Whylogs
Logging
Integration fully implemented for data logging
whylogs
✅ 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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