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Stack components are the building blocks of a ZenML stack. Each component represents a different category of infrastructure or tooling that your ML workflows need. Understanding these components helps you build the right infrastructure for your use case.

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Component Types

ZenML defines several stack component types, each serving a specific purpose in the ML workflow:

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Required Components

Every stack must include these two components:

Orchestrator

Controls pipeline execution, scheduling, and step coordination. Determines where and how your pipeline runs.

Artifact Store

Stores all artifacts (data, models, etc.) produced by pipeline steps. Provides versioned storage.

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Optional Components

These components add specialized functionality:

Container Registry

Stores Docker images for containerized step execution.

Step Operator

Runs specific steps on specialized infrastructure (GPUs, distributed compute).

Experiment Tracker

Logs metrics, parameters, and artifacts for experiment tracking.

Model Deployer

Deploys trained models as prediction services.

Model Registry

Manages model versions, metadata, and lifecycle stages.

Feature Store

Serves features for training and real-time inference.

Data Validator

Validates data quality and schema compliance.

Alerter

Sends notifications via Slack, email, or other channels.

Annotator

Manages data annotation workflows and labeling.

Image Builder

Builds Docker images for containerized execution.

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Orchestrators

Orchestrators are the brain of your pipeline execution. They determine how steps are executed and scheduled.

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Available Orchestrators

# Local Orchestrator - Run in your local environment
zenml orchestrator register local_orch --flavor=local

# Kubernetes - Run on Kubernetes cluster
zenml orchestrator register k8s_orch \
    --flavor=kubernetes \
    --kubernetes_context=my-cluster \
    --kubernetes_namespace=zenml

# Kubeflow - Run on Kubeflow Pipelines
zenml orchestrator register kfp_orch \
    --flavor=kubeflow \
    --kubeflow_hostname=https://kubeflow.example.com

# Vertex AI - Run on Google Cloud Vertex AI
zenml orchestrator register vertex_orch \
    --flavor=vertex \
    --project=my-gcp-project \
    --location=us-central1

# Airflow - Run on Apache Airflow
zenml orchestrator register airflow_orch \
    --flavor=airflow \
    --airflow_home=/path/to/airflow

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Using Orchestrators

from zenml import pipeline

@pipeline
def my_pipeline():
    """Pipeline runs on the active stack's orchestrator."""
    step_one()
    step_two()

# The orchestrator determines execution:
# - Local: Sequential execution in current process
# - Kubernetes: Distributed execution across pods
# - Vertex AI: Managed execution on Google Cloud

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Artifact Stores

Artifact stores provide persistent storage for all pipeline artifacts.

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Available Artifact Stores

# Local - Store on local filesystem
zenml artifact-store register local_store \
    --flavor=local \
    --path=/path/to/artifacts

# AWS S3 - Store on Amazon S3
zenml artifact-store register s3_store \
    --flavor=s3 \
    --path=s3://my-bucket/artifacts

# Google Cloud Storage - Store on GCS
zenml artifact-store register gcs_store \
    --flavor=gcp \
    --path=gs://my-bucket/artifacts

# Azure Blob Storage - Store on Azure
zenml artifact-store register azure_store \
    --flavor=azure \
    --path=az://my-container/artifacts

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Accessing Artifact Store

from zenml.client import Client

client = Client()
artifact_store = client.active_stack.artifact_store

print(f"Artifacts stored at: {artifact_store.path}")
print(f"Flavor: {artifact_store.flavor}")

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Container Registries

Container registries store Docker images for containerized step execution.

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Available Container Registries

# DockerHub
zenml container-registry register dockerhub \
    --flavor=dockerhub \
    --uri=docker.io/myusername

# AWS ECR
zenml container-registry register ecr \
    --flavor=aws \
    --uri=123456789.dkr.ecr.us-east-1.amazonaws.com

# Google Artifact Registry
zenml container-registry register gar \
    --flavor=gcp \
    --uri=us-central1-docker.pkg.dev/my-project/my-repo

# Azure Container Registry
zenml container-registry register acr \
    --flavor=azure \
    --uri=myregistry.azurecr.io

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When Are Containers Used?

Containers are built when:
  • Running on remote orchestrators (Kubernetes, Vertex AI, etc.)
  • Using step operators
  • Explicitly configured via Docker settings
from zenml import pipeline
from zenml.config import DockerSettings

@pipeline(
    settings={
        "docker": DockerSettings(
            requirements=["tensorflow==2.13.0"],
            parent_image="tensorflow/tensorflow:latest-gpu"
        )
    }
)
def containerized_pipeline():
    """This pipeline runs in containers."""
    train_model()

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Step Operators

Step operators run individual steps on specialized infrastructure.

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Use Cases

  • GPU-intensive training steps
  • Distributed computation
  • Steps requiring different resources than the orchestrator

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Available Step Operators

# Kubernetes
zenml step-operator register k8s_step_op \
    --flavor=kubernetes \
    --kubernetes_context=my-cluster \
    --kubernetes_namespace=training

# Vertex AI
zenml step-operator register vertex_step_op \
    --flavor=vertex \
    --project=my-project \
    --region=us-central1 \
    --machine_type=n1-highmem-16

# SageMaker
zenml step-operator register sagemaker_step_op \
    --flavor=sagemaker \
    --role=arn:aws:iam::123456789:role/SageMakerRole \
    --instance_type=ml.p3.2xlarge

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Using Step Operators

from zenml import step
from zenml.config import ResourceSettings

@step(
    step_operator="vertex_step_op",
    settings={
        "resources": ResourceSettings(
            cpu_count=8,
            gpu_count=2,
            memory="32GB"
        )
    }
)
def gpu_training_step(data: pd.DataFrame) -> Any:
    """This step runs on Vertex AI with 2 GPUs."""
    model = train_on_gpu(data)
    return model

@step
def regular_step() -> pd.DataFrame:
    """This step runs on the orchestrator."""
    return load_data()

@pipeline
def hybrid_pipeline():
    data = regular_step()  # Runs on orchestrator
    model = gpu_training_step(data)  # Runs on step operator

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Experiment Trackers

Experiment trackers log metrics, parameters, and artifacts during training.

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Available Experiment Trackers

# MLflow
zenml experiment-tracker register mlflow_tracker \
    --flavor=mlflow \
    --tracking_uri=http://localhost:5000

# Weights & Biases
zenml experiment-tracker register wandb_tracker \
    --flavor=wandb \
    --entity=my-team \
    --project=my-project

# Neptune
zenml experiment-tracker register neptune_tracker \
    --flavor=neptune \
    --project=my-workspace/my-project

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Using Experiment Trackers

from zenml import step
from zenml.client import Client
import mlflow

@step(experiment_tracker="mlflow_tracker")
def training_step(data: pd.DataFrame) -> Any:
    """Step with experiment tracking."""
    
    # MLflow is automatically configured
    mlflow.log_param("learning_rate", 0.01)
    mlflow.log_param("batch_size", 32)
    
    model = train_model(data)
    
    mlflow.log_metric("accuracy", 0.95)
    mlflow.log_metric("loss", 0.05)
    
    return model

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Model Deployers

Model deployers deploy trained models as prediction services.

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Available Model Deployers

# Seldon Core
zenml model-deployer register seldon_deployer \
    --flavor=seldon \
    --kubernetes_context=my-cluster

# BentoML
zenml model-deployer register bentoml_deployer \
    --flavor=bentoml

# MLflow
zenml model-deployer register mlflow_deployer \
    --flavor=mlflow

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Deploying Models

from zenml import step, pipeline
from zenml.integrations.seldon.steps import seldon_model_deployer_step

@step
def train_step() -> Any:
    model = train_model()
    return model

@pipeline
def deployment_pipeline():
    model = train_step()
    
    # Deploy model as a service
    seldon_model_deployer_step(
        model=model,
        service_config={"name": "my-model-service"}
    )

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Model Registries

Model registries manage model versions and metadata.

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Available Model Registries

# MLflow
zenml model-registry register mlflow_registry \
    --flavor=mlflow \
    --uri=http://localhost:5000

# AWS SageMaker
zenml model-registry register sagemaker_registry \
    --flavor=sagemaker \
    --region=us-east-1

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Using Model Registry

from zenml import step, Model
from zenml.client import Client

@step
def register_model_step(model: Any) -> None:
    """Register model in the registry."""
    client = Client()
    
    # Model is automatically registered if model registry is in stack
    model_registry = client.active_stack.model_registry
    
    if model_registry:
        model_registry.register_model(
            name="my_model",
            version="v1.0",
            model_source_uri="path/to/model"
        )

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Feature Stores

Feature stores serve features for training and inference.

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Available Feature Stores

# Feast
zenml feature-store register feast_store \
    --flavor=feast \
    --feast_repo=/path/to/feast/repo

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Using Feature Stores

from zenml import step

@step
def get_features_step(entity_ids: list) -> pd.DataFrame:
    """Retrieve features from feature store."""
    from zenml.client import Client
    
    feature_store = Client().active_stack.feature_store
    
    features = feature_store.get_online_features(
        feature_refs=["feature_view:feature_1", "feature_view:feature_2"],
        entity_rows=[{"entity_id": id} for id in entity_ids]
    )
    
    return features.to_df()

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Data Validators

Data validators check data quality and schema.

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Available Data Validators

# Great Expectations
zenml data-validator register ge_validator \
    --flavor=great_expectations

# Evidently
zenml data-validator register evidently_validator \
    --flavor=evidently

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Using Data Validators

from zenml import step
import pandas as pd

@step
def validate_data_step(data: pd.DataFrame) -> pd.DataFrame:
    """Validate data quality."""
    from zenml.integrations.great_expectations.steps import (
        great_expectations_validator_step
    )
    
    # Define expectations
    expectations = {
        "expect_column_to_exist": {"column": "feature_1"},
        "expect_column_values_to_not_be_null": {"column": "feature_1"}
    }
    
    # Validation happens automatically if data validator in stack
    return data

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Alerters

Alerters send notifications when pipelines succeed or fail.

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Available Alerters

# Slack
zenml alerter register slack_alerter \
    --flavor=slack \
    --slack_token={{slack.token}} \
    --default_slack_channel_id=C1234567890

# Discord
zenml alerter register discord_alerter \
    --flavor=discord \
    --discord_webhook_url={{discord.webhook}}

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Using Alerters

from zenml import pipeline, step

def on_failure(exception: BaseException):
    """Send alert on failure."""
    from zenml.client import Client
    
    alerter = Client().active_stack.alerter
    if alerter:
        alerter.post(
            message=f"Pipeline failed: {exception}",
            params={"title": "Pipeline Failure"}
        )

@pipeline(on_failure=on_failure)
def monitored_pipeline():
    risky_step()

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Component Configuration

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View Component Details

# List all components of a type
zenml orchestrator list

# Describe specific component
zenml orchestrator describe kubernetes_orch

# Update component
zenml orchestrator update kubernetes_orch \
    --kubernetes_namespace=production

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Programmatic Access

from zenml.client import Client
from zenml.enums import StackComponentType

client = Client()

# List all orchestrators
orchestrators = client.list_stack_components(
    component_type=StackComponentType.ORCHESTRATOR
)

for orch in orchestrators:
    print(f"Orchestrator: {orch.name} ({orch.flavor})")

# Get specific component
artifact_store = client.get_stack_component(
    component_type=StackComponentType.ARTIFACT_STORE,
    name_id_or_prefix="s3_store"
)

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Component Flavors

Each component type has multiple flavors (implementations): 
from zenml.client import Client
from zenml.enums import StackComponentType

client = Client()

# List available flavors for a component type
flavors = client.list_flavors(
    component_type=StackComponentType.ORCHESTRATOR
)

for flavor in flavors:
    print(f"Flavor: {flavor.name}")
    print(f"Integration: {flavor.integration}")

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Best Practices

Start Simple

Begin with local components (local orchestrator, local artifact store) and graduate to cloud components as needed.

Match Your Workflow

Choose components that fit your actual needs. Don’t over-engineer with components you don’t use.

Test Incrementally

Add one new component at a time and validate it works before adding more.

Use Integrations

Install ZenML integrations to get pre-built components: zenml integration install <integration>
  • Stacks - Learn how components combine into stacks
  • Pipelines - Understand how pipelines use components
  • Steps - Configure steps for specific components

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Code Reference

  • StackComponent base class: src/zenml/stack/stack_component.py:67
  • StackComponentType enum: src/zenml/enums.py:158
  • Component flavors: src/zenml/stack/flavor.py

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