# ZenML

## Docs

- [Docker and Containerization](https://mintlify.wiki/zenml-io/zenml/advanced/containerization.md): Package ZenML pipelines with Docker for reproducible, portable execution
- [Custom Artifact Materializers](https://mintlify.wiki/zenml-io/zenml/advanced/custom-materializers.md): Create custom materializers to handle any data type in ZenML pipelines
- [Building Custom Orchestrators](https://mintlify.wiki/zenml-io/zenml/advanced/custom-orchestrators.md): Learn how to build custom orchestrators for ZenML pipelines
- [Dynamic Pipeline Execution](https://mintlify.wiki/zenml-io/zenml/advanced/dynamic-pipelines.md): Build pipelines where the execution graph is determined at runtime
- [Resource Configuration](https://mintlify.wiki/zenml-io/zenml/advanced/resource-configuration.md): Configure CPU, memory, and GPU resources for pipeline steps
- [Agent Evaluation](https://mintlify.wiki/zenml-io/zenml/agents/agent-evaluation.md): Build reproducible evaluation pipelines to systematically compare agent architectures and configurations
- [Agent Framework Integrations](https://mintlify.wiki/zenml-io/zenml/agents/agent-frameworks.md): Integration patterns for LangGraph, CrewAI, LangChain, LlamaIndex, and 8 other popular agent frameworks
- [Agent Architecture Comparison](https://mintlify.wiki/zenml-io/zenml/agents/examples/agent-comparison.md): Complete example comparing SingleAgentRAG, MultiSpecialist, and LangGraph architectures on customer service queries
- [Deploying AI Agents](https://mintlify.wiki/zenml-io/zenml/agents/examples/deploying-agents.md): Deploy agents as production HTTP services with embedded web interfaces using ZenML
- [Framework Integration Examples](https://mintlify.wiki/zenml-io/zenml/agents/examples/framework-integrations.md): Working examples for LangGraph, CrewAI, LangChain, LlamaIndex, PydanticAI, and 7 other agent frameworks
- [Orchestrating AI Agents](https://mintlify.wiki/zenml-io/zenml/agents/orchestrating-agents.md): Patterns and best practices for building production-ready agent workflows with ZenML
- [AI Agent Orchestration with ZenML](https://mintlify.wiki/zenml-io/zenml/agents/overview.md): Build, deploy, and monitor production-ready AI agents with ZenML's orchestration framework
- [add_tags](https://mintlify.wiki/zenml-io/zenml/api/add-tags.md): Add tags to ZenML resources
- [ArtifactConfig](https://mintlify.wiki/zenml-io/zenml/api/artifact-config.md): Configure artifact properties in step outputs
- [BaseOrchestrator](https://mintlify.wiki/zenml-io/zenml/api/base-orchestrator.md): Base class for all ZenML orchestrators that coordinate pipeline execution.
- [BaseStep](https://mintlify.wiki/zenml-io/zenml/api/base-step.md): Abstract base class for defining pipeline steps in ZenML.
- [bulk_log_metadata](https://mintlify.wiki/zenml-io/zenml/api/bulk-log-metadata.md): Log metadata for multiple resources in a single call
- [ExternalArtifact](https://mintlify.wiki/zenml-io/zenml/api/external-artifact.md): Use external values as step inputs without writing additional steps
- [get_pipeline_context](https://mintlify.wiki/zenml-io/zenml/api/get-pipeline-context.md): Access pipeline configuration during pipeline definition
- [get_step_context](https://mintlify.wiki/zenml-io/zenml/api/get-step-context.md): Access step runtime information during step execution
- [link_artifact_to_model](https://mintlify.wiki/zenml-io/zenml/api/link-artifact-to-model.md): Link artifacts to model versions
- [load_artifact](https://mintlify.wiki/zenml-io/zenml/api/load-artifact.md): Load artifacts from the artifact store
- [log_artifact_metadata](https://mintlify.wiki/zenml-io/zenml/api/log-artifact-metadata.md): Log metadata to artifact versions (deprecated)
- [log_metadata](https://mintlify.wiki/zenml-io/zenml/api/log-metadata.md): Log metadata to steps, runs, artifacts, and models
- [log_model_metadata](https://mintlify.wiki/zenml-io/zenml/api/log-model-metadata.md): Log metadata to model versions (deprecated)
- [log_step_metadata](https://mintlify.wiki/zenml-io/zenml/api/log-step-metadata.md): Log metadata to step runs during pipeline execution
- [Model](https://mintlify.wiki/zenml-io/zenml/api/model.md): Model class for ZenML's Model Control Plane
- [@pipeline](https://mintlify.wiki/zenml-io/zenml/api/pipeline.md): Decorator to create a ZenML pipeline
- [register_artifact](https://mintlify.wiki/zenml-io/zenml/api/register-artifact.md): Register existing data in the artifact store as a ZenML artifact
- [remove_tags](https://mintlify.wiki/zenml-io/zenml/api/remove-tags.md): Remove tags from ZenML resources
- [ResourceSettings](https://mintlify.wiki/zenml-io/zenml/api/resource-settings.md): Configure hardware resources for steps
- [save_artifact](https://mintlify.wiki/zenml-io/zenml/api/save-artifact.md): Manually save artifacts to the artifact store
- [Schedule](https://mintlify.wiki/zenml-io/zenml/api/schedule.md): Schedule pipeline runs with cron expressions or intervals
- [show](https://mintlify.wiki/zenml-io/zenml/api/show.md): Open the ZenML dashboard in your browser
- [Stack](https://mintlify.wiki/zenml-io/zenml/api/stack.md): ZenML Stack class representing a collection of stack components for running ML pipelines.
- [StackComponent](https://mintlify.wiki/zenml-io/zenml/api/stack-component.md): Base class for all ZenML stack components that provide infrastructure and tooling for pipelines.
- [@step](https://mintlify.wiki/zenml-io/zenml/api/step.md): Decorator to create a ZenML step
- [Tag](https://mintlify.wiki/zenml-io/zenml/api/tag.md): Tag class for creating rich tags with colors and properties
- [unmapped](https://mintlify.wiki/zenml-io/zenml/api/unmapped.md): Pass inputs to parallel steps without mapping over them
- [zenml deployment](https://mintlify.wiki/zenml-io/zenml/cli/deployment.md): Manage long-running pipeline deployments via the CLI.
- [zenml init](https://mintlify.wiki/zenml-io/zenml/cli/init.md): Initialize a ZenML repository in your project directory.
- [zenml integration](https://mintlify.wiki/zenml-io/zenml/cli/integration.md): Install and manage ZenML integrations for third-party tools and cloud providers.
- [zenml login](https://mintlify.wiki/zenml-io/zenml/cli/login.md): Connect to ZenML Pro, self-hosted servers, or start a local server.
- [CLI Overview](https://mintlify.wiki/zenml-io/zenml/cli/overview.md): Learn how to use the ZenML command-line interface for pipeline orchestration and MLOps workflows.
- [zenml pipeline](https://mintlify.wiki/zenml-io/zenml/cli/pipeline.md): Manage pipelines, runs, builds, and deployments via the CLI.
- [zenml stack](https://mintlify.wiki/zenml-io/zenml/cli/stack.md): Manage MLOps stacks and their components via the CLI.
- [Artifact Stores](https://mintlify.wiki/zenml-io/zenml/components/artifact-stores.md): Store and manage pipeline artifacts, inputs, and outputs
- [Container Registries](https://mintlify.wiki/zenml-io/zenml/components/container-registries.md): Store and manage Docker container images for containerized execution
- [Experiment Trackers](https://mintlify.wiki/zenml-io/zenml/components/experiment-trackers.md): Track ML experiments, metrics, and parameters across pipeline runs
- [Model Deployers](https://mintlify.wiki/zenml-io/zenml/components/model-deployers.md): Deploy models for online inference and serving
- [Orchestrators](https://mintlify.wiki/zenml-io/zenml/components/orchestrators.md): Execute and manage ML pipelines across different environments
- [Stack Components Overview](https://mintlify.wiki/zenml-io/zenml/components/overview.md): Understanding ZenML's modular stack architecture
- [Step Operators](https://mintlify.wiki/zenml-io/zenml/components/step-operators.md): Run individual pipeline steps on specialized infrastructure
- [Artifacts](https://mintlify.wiki/zenml-io/zenml/concepts/artifacts.md): Understanding ZenML's artifact system for data versioning and lineage
- [Models](https://mintlify.wiki/zenml-io/zenml/concepts/models.md): Understanding ZenML's Model Control Plane for ML model management
- [Pipelines](https://mintlify.wiki/zenml-io/zenml/concepts/pipelines.md): Understanding ZenML pipelines and how they orchestrate ML workflows
- [Stack Components](https://mintlify.wiki/zenml-io/zenml/concepts/stack-components.md): Understanding ZenML stack components and their roles
- [Stacks](https://mintlify.wiki/zenml-io/zenml/concepts/stacks.md): Understanding ZenML stacks and infrastructure abstraction
- [Steps](https://mintlify.wiki/zenml-io/zenml/concepts/steps.md): Steps are the building blocks of ZenML pipelines
- [Docker Deployment](https://mintlify.wiki/zenml-io/zenml/deployment/docker.md): Deploy ZenML server using Docker for containerized environments
- [Kubernetes Deployment](https://mintlify.wiki/zenml-io/zenml/deployment/kubernetes.md): Deploy ZenML server on Kubernetes using Helm charts for production environments
- [Deployment Overview](https://mintlify.wiki/zenml-io/zenml/deployment/overview.md): Understanding ZenML server deployment architecture and options
- [Server Deployment Options](https://mintlify.wiki/zenml-io/zenml/deployment/server-deployment.md): Deploy ZenML server using different methods for various use cases
- [Artifact Management](https://mintlify.wiki/zenml-io/zenml/guides/artifacts/artifact-management.md): Learn how ZenML tracks, versions, and manages pipeline artifacts
- [Creating Pipelines](https://mintlify.wiki/zenml-io/zenml/guides/pipelines/creating-pipelines.md): Learn how to create and structure ZenML pipelines
- [Deploying Pipelines](https://mintlify.wiki/zenml-io/zenml/guides/pipelines/deploying-pipelines.md): Deploy ZenML pipelines as long-running services
- [Scheduling Pipelines](https://mintlify.wiki/zenml-io/zenml/guides/pipelines/scheduling-pipelines.md): Automate pipeline execution with recurring schedules
- [Configuring Stacks](https://mintlify.wiki/zenml-io/zenml/guides/stacks/configuring-stacks.md): Learn how to configure and customize ZenML stacks
- [Stack Switching](https://mintlify.wiki/zenml-io/zenml/guides/stacks/stack-switching.md): Switch between different ZenML stacks for various environments and use cases
- [Step Context](https://mintlify.wiki/zenml-io/zenml/guides/steps/step-context.md): Access runtime information and metadata within pipeline steps
- [Writing Steps](https://mintlify.wiki/zenml-io/zenml/guides/steps/writing-steps.md): Learn how to write effective ZenML pipeline steps
- [Installation](https://mintlify.wiki/zenml-io/zenml/installation.md): Install ZenML and set up your development environment for local or production use
- [AWS Integration](https://mintlify.wiki/zenml-io/zenml/integrations/aws.md): Connect ZenML to Amazon Web Services for scalable ML infrastructure
- [Azure Integration](https://mintlify.wiki/zenml-io/zenml/integrations/azure.md): Run ZenML pipelines on Microsoft Azure with AzureML
- [Google Cloud Integration](https://mintlify.wiki/zenml-io/zenml/integrations/gcp.md): Run ZenML pipelines on Google Cloud Platform with Vertex AI
- [Kubeflow Integration](https://mintlify.wiki/zenml-io/zenml/integrations/kubeflow.md): Run ZenML pipelines with Kubeflow Pipelines orchestration
- [Kubernetes Integration](https://mintlify.wiki/zenml-io/zenml/integrations/kubernetes.md): Run ZenML pipelines natively on Kubernetes clusters
- [MLflow Integration](https://mintlify.wiki/zenml-io/zenml/integrations/mlflow.md): Track experiments and manage models with MLflow
- [Integration System Overview](https://mintlify.wiki/zenml-io/zenml/integrations/overview.md): Extend ZenML with cloud platforms, ML tools, and orchestrators
- [AWS SageMaker Integration](https://mintlify.wiki/zenml-io/zenml/integrations/sagemaker.md): Run ZenML pipelines on AWS SageMaker with orchestration and step operators
- [GCP Vertex AI Integration](https://mintlify.wiki/zenml-io/zenml/integrations/vertex-ai.md): Run ZenML pipelines on Google Cloud Vertex AI
- [Weights & Biases Integration](https://mintlify.wiki/zenml-io/zenml/integrations/wandb.md): Track experiments with Weights & Biases (W&B)
- [Introduction to ZenML](https://mintlify.wiki/zenml-io/zenml/introduction.md): One AI Platform From Pipelines to Agents - Build production-ready ML pipelines and AI workflows that run anywhere
- [Quickstart](https://mintlify.wiki/zenml-io/zenml/quickstart.md): Build and run your first ZenML pipeline in 5 minutes - Learn core concepts through a hands-on example