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Your model can work perfectly in a notebook and still be difficult to rerun, audit, share, or deploy. ZenML addresses that gap as an open-source, Python-based MLOps framework and metadata layer: you define reusable steps and pipelines, while configurable stacks connect those pipelines to orchestrators, artifact stores, trackers, and deployment infrastructure. It is a coordination layer, not a replacement for your cloud account, database, GPU cluster, serving system, or monitoring strategy.
This guide explains the core concepts, builds a small scikit-learn pipeline, and shows how to progress from local experimentation to team-scale operation.
What problem does ZenML solve?
Notebook-based machine learning becomes fragile when a team needs repeatable training, traceable data and code, scheduled jobs, remote execution, or a shared view of results. ZenML gives those activities a consistent workflow model and records metadata about pipeline runs.
- Run the same training process reliably instead of copying notebook cells.
- Associate outputs with the code, parameters, inputs, and environment that produced them.
- Move from a laptop to Docker, Kubernetes, or a cloud backend without rewriting the model logic.
- Connect orchestration, artifact storage, experiment tracking, deployment, and other services.
- Give a team a central view of runs, artifacts, logs, metrics, and pipeline history.
ZenML does not automatically supply datasets, feature stores, production databases, compute clusters, model-serving fleets, or monitoring policies. Those remain architectural responsibilities for your team.
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ZenML is released under the Apache License 2.0. The latest release checked for this guide is 0.96.3, released August 7, 2026; verify the current release before copying version-sensitive commands from any tutorial. See the release history.
ZenML in plain English
ZenML’s core model is deliberately small:
| Concept | Meaning |
|---|---|
| Step | A reusable Python function, such as loading data, training a model, or calculating a metric. Steps are declared with @step. |
| Pipeline | A directed workflow that connects steps. ZenML derives a dependency graph (DAG) from the function calls inside a @pipeline. |
| Artifact | A persisted, versioned output such as a dataset, model, prediction file, embedding, or report. |
| Stack | The infrastructure configuration used for a run: at minimum an orchestrator and an artifact store, plus optional components. |
| Orchestrator | The execution engine that schedules and runs steps. |
| Artifact store | The location where step outputs are materialized and retained. |
| Server | A central REST-based metadata service used for collaboration and remote workloads. |
| Dashboard | The visual interface for runs, pipelines, artifacts, stacks, logs, metrics, and timelines. |
These definitions and the execution model are documented in ZenML’s core concepts and the stacks overview.
Prerequisites
- Basic Python: functions, imports, virtual environments, and type annotations.
- A fresh virtual environment for each project.
- scikit-learn for the example below.
- Docker is useful for a local server or containers, but is not required for the simplest local tutorial.
- Cloud credentials and remote infrastructure only when you choose a remote stack.
Do not assume a universal Python-version range from an old blog post. Check compatibility for the ZenML version you install; the 0.95.0 release notes, for example, mention Python 3.14 support.
Install ZenML locally
Use the local extra in a new project directory:
python -m venv .venv
source .venv/bin/activate # macOS/Linux
# .venvScriptsactivate # Windows PowerShell
python -m pip install --upgrade pip
pip install "zenml[local]"
pip install scikit-learnThe current getting-started path recommends zenml[local]; see ZenML’s getting-started page. Then initialize the repository:
zenml initFor a local server-backed setup, try:
zenml login --localLocal-login behavior and required extras can change between releases. If a command differs, run zenml --help, zenml --version, and consult the current documentation. The repository also describes a server-capable installation using pip install "zenml[server]".
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Build a first pipeline
Save this as run_pipeline.py. It demonstrates typed step outputs, model training, evaluation, and a pipeline entry point. It is an instructional example; confirm API details against Your First AI Pipeline when using a newer SDK.
from zenml import pipeline, step
from sklearn.datasets import load_iris
from sklearn.svm import SVC
from sklearn.metrics import accuracy_score
@step
def load_data() -> tuple[list, list]:
X, y = load_iris(return_X_y=True)
return X.tolist(), y.tolist()
@step
def train_model(X: list, y: list) -> SVC:
model = SVC()
model.fit(X, y)
return model
@step
def evaluate_model(model: SVC, X: list, y: list) -> float:
predictions = model.predict(X)
return float(accuracy_score(y, predictions))
@pipeline
def training_pipeline():
X, y = load_data()
model = train_model(X, y)
evaluate_model(model, X, y)
if __name__ == "__main__":
training_pipeline()Run it with:
python run_pipeline.pyThe annotations help ZenML understand inputs and outputs; they are not merely documentation. Returned values can become tracked artifacts or other materialized outputs, provided ZenML has a suitable materializer and configuration. A Python object that exists only in process memory is not the same as a persisted artifact. Large external files may be referenced through metadata rather than copied into the artifact store.
Inspect the run and artifacts
Open the dashboard supplied by your local setup, or use the CLI and server appropriate to your version. You should be able to inspect the pipeline DAG, each step’s status and logs, output artifacts, metrics, run metadata, timing, and a timeline that helps reveal slow steps. The dashboard concepts are described in the first-pipeline guide.
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Reproducibility is improved, not guaranteed. Results can still change when data, dependency versions, base images, random seeds, hardware, external APIs, or nondeterministic algorithms change. Pin dependencies, version input data, set seeds where appropriate, and use containerized environments for serious workflows.
Stacks: the abstraction that makes execution portable
A stack keeps pipeline code separate from execution infrastructure. A minimal stack has:
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- An orchestrator.
- An artifact store.
It can also include a container registry, experiment tracker, model deployer or deployment component, secrets manager, step operator, and cloud-specific integrations. The same pipeline can therefore use a local stack while learning, a Docker stack for reproducible packaging, and Kubernetes or a cloud stack for remote runs.
Portability is a goal, not a promise of identical behavior. A Kubernetes stack still needs a Kubernetes cluster; a cloud stack still needs permissions, networking, storage, and compute. Backend-specific scheduling, GPU topology, and distributed-training features may require deliberate configuration.
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↓
ZenML pipeline
↓
ZenML stack
┌──────────────┬──────────────┬──────────────┐
│ Orchestrator │ Artifact │ Optional │
│ │ store │ integrations │
└──────────────┴──────────────┴──────────────┘
↓
Local, Docker, Kubernetes, or cloud executionLocal, self-hosted, or managed?
Local development
Local deployment uses a local SQLite metadata store and is intended for learning, experimentation, and personal projects. SQLite is convenient but not a durable shared production database.
Self-hosted ZenML Server
A server centralizes metadata for multiple developers and remote workloads. ZenML’s deployment guidance uses a persistent database such as MySQL for robust workloads; its Docker guide shows the zenmldocker/zenml-server image and database configuration. See deployment options and the Docker deployment guide.
ZenML Pro
Pro is a managed control plane for teams that want less platform maintenance, collaboration features, enterprise identity controls, or air-gapped options. ZenML states that customer data, artifacts, and compute remain in the customer’s environment. The pricing page displayed a Scale plan at $999 per month with execution-based billing when checked August 18, 2026; prices and included tiers can change. Open-source ZenML is free, but storage, databases, registries, GPUs, cloud compute, and engineering time are not.
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Add tracking and integrations
ZenML is not “MLflow with extra steps.” MLflow commonly centers on experiment tracking, model packaging, and registry functions; ZenML centers on pipeline orchestration, metadata, infrastructure abstraction, and workflow coordination. They can be combined, as can Weights & Biases and other trackers.
Integrations are organized by function:
- Orchestration: local execution, Docker, Kubernetes, Kubeflow, and cloud backends.
- Storage: local filesystems, object stores, and S3-compatible services.
- Tracking: MLflow, Weights & Biases, Trackio, and others.
- Cloud execution: Amazon SageMaker, Google Vertex AI, Azure ML, and related services.
- LLM and agent workflows: LangGraph, Langfuse, and ecosystem tools.
Examples such as Trackio, Backblaze B2, Baseten, and OAuth2 connectors appear in the 0.96.3 release notes; treat that list as version-specific, not exhaustive.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.Deployment: batch jobs versus online services
Batch execution
Scheduled training, data preparation, evaluation, and batch inference are ordinary pipeline runs. They can be triggered by a scheduler supplied by your chosen infrastructure.
Long-running pipeline deployment
ZenML’s current deployment concept can expose a pipeline as a long-lived HTTP service for request-response workloads such as real-time inference or interactive AI applications. Documentation is moving away from treating specialized Model Deployer components as the universal path, although specialized integrations may still provide optimized serving. Read the pipeline deployment documentation.
An HTTP endpoint is not automatically a hardened model-serving platform. Plan authentication, input validation, timeouts, cold-start behavior, autoscaling, observability, rollback, privacy, availability, and cost controls.
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ZenML compared with alternatives
| Option | Strongest fit | Important trade-off |
|---|---|---|
| ZenML | Portable ML pipelines, metadata, and integration across execution backends. | You still operate or pay for the underlying infrastructure. |
| MLflow | Experiment tracking, model registry, and lifecycle workflows. | It is not primarily an infrastructure-portable pipeline layer, although it integrates with ZenML. |
| Kubeflow | Kubernetes-native ML workflows for teams already running Kubernetes. | Cluster operations remain complex. |
| Managed cloud ML | Provider-managed compute and integrated cloud services. | Greater cloud coupling, permissions work, and service costs. |
| Dagster, Airflow, or Prefect | Broad data and software workflow orchestration. | ML-specific artifact and model integrations may require additional tooling. |
Troubleshooting checklist
Installation or CLI errors
Use a clean environment, upgrade pip, and inspect the installed package:
python -m pip install --upgrade pip
python -m pip show zenml
zenml --version
zenml --helpWrong project or no active stack
Run zenml init in the intended project root. Confirm the selected stack in the CLI or dashboard. A pipeline cannot run without an orchestrator and artifact store.
Serialization and materialization failures
Return simple typed values where possible. Custom classes must be importable in the execution environment and may need a configured materializer. Pin dependencies and keep local and remote images consistent.
Artifact-store or remote-run failures
Check credentials, bucket permissions, region, endpoint, network access, secrets, and service connectors. Diagnose in layers: client connectivity, server authentication, stack configuration, orchestrator scheduling, image build and registry access, artifact-store access, then application code.
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When ZenML is a good choice
- You need reusable pipelines rather than one-off notebooks.
- Execution may move from local machines to remote infrastructure.
- You want shared metadata and artifact history.
- Existing tools such as MLflow or W&B should remain part of the workflow.
- You prefer an open-source, self-hosted starting point before adopting a managed control plane.
Choose another approach when you only need simple experiment tracking, want a completely managed end-to-end cloud platform with minimal configuration, already operate a mature internal platform, or require specialized serving that a general pipeline deployment does not provide. The practical first step for most learners is the free local path; move to a shared server when collaboration, persistence, or remote execution becomes a real requirement.
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