# ZenML - Bridging the gap between ML & Ops

## Documentation

- [Welcome to ZenML](https://docs.zenml.io/getting-started/introduction): Discover resources to build, deploy, and scale your ML pipelines with ZenML.
- [Installation](https://docs.zenml.io/getting-started/installation): Installing ZenML and getting started.
- [Core concepts](https://docs.zenml.io/getting-started/core-concepts): Discovering the core concepts behind ZenML.
- [System Architecture](https://docs.zenml.io/getting-started/system-architectures): Different variations of the ZenML architecture depending on your needs.
- [Deploying ZenML](https://docs.zenml.io/getting-started/deploying-zenml): Why do we need to deploy ZenML?
- [Deploy with Docker](https://docs.zenml.io/getting-started/deploying-zenml/deploy-with-docker): Deploying ZenML in a Docker container.
- [Deploy with Helm](https://docs.zenml.io/getting-started/deploying-zenml/deploy-with-helm): Deploying ZenML in a Kubernetes cluster with Helm.
- [Deploy using HuggingFace Spaces](https://docs.zenml.io/getting-started/deploying-zenml/deploy-using-huggingface-spaces): Deploying ZenML to Huggingface Spaces.
- [Deploy with custom images](https://docs.zenml.io/getting-started/deploying-zenml/deploy-with-custom-image): Deploying ZenML with custom Docker images.
- [Secret management](https://docs.zenml.io/getting-started/deploying-zenml/secret-management): Configuring the secrets store.
- [Custom secret stores](https://docs.zenml.io/getting-started/deploying-zenml/custom-secret-stores): Learning how to develop a custom secret store.
- [Manage your ZenML server](https://docs.zenml.io/how-to/manage-zenml-server)
- [Connect to a server](https://docs.zenml.io/how-to/manage-zenml-server/connecting-to-zenml): Various means of connecting to ZenML.
- [Connect in with your User (interactive)](https://docs.zenml.io/how-to/manage-zenml-server/connecting-to-zenml/connect-in-with-your-user-interactive): Connect to the ZenML server using the ZenML CLI and the web based login.
- [Connect with an API Token](https://docs.zenml.io/how-to/manage-zenml-server/connecting-to-zenml/connect-with-an-api-token): Connect to the ZenML server using a temporary API token.
- [Connect with a Service Account](https://docs.zenml.io/how-to/manage-zenml-server/connecting-to-zenml/connect-with-a-service-account): Connect to the ZenML server using a service account and an API key.
- [Upgrade your ZenML server](https://docs.zenml.io/how-to/manage-zenml-server/upgrade-zenml-server): Learn how to upgrade your server to a new version of ZenML for the different deployment options.
- [Best practices for upgrading ZenML](https://docs.zenml.io/how-to/manage-zenml-server/best-practices-upgrading-zenml): Learn about best practices for upgrading your ZenML server and your code.
- [Using ZenML server in production](https://docs.zenml.io/how-to/manage-zenml-server/using-zenml-server-in-prod): Learn about best practices for using ZenML server in production environments.
- [Use the ZenML VSCode extension](https://docs.zenml.io/how-to/manage-zenml-server/vscode-extension): Use the ZenML VSCode extension to manage your ZenML server
- [Chat with your ZenML server](https://docs.zenml.io/how-to/manage-zenml-server/mcp-chat-with-server): Chat with your ZenML server
- [Troubleshoot your ZenML server](https://docs.zenml.io/how-to/manage-zenml-server/troubleshoot-your-deployed-server): Troubleshooting tips for your ZenML deployment
- [Migration guide](https://docs.zenml.io/how-to/manage-zenml-server/migration-guide): How to migrate your ZenML code to the newest version.
- [Migration guide 0.13.2 → 0.20.0](https://docs.zenml.io/how-to/manage-zenml-server/migration-guide/migration-zero-twenty): How to migrate from ZenML <=0.13.2 to 0.20.0.
- [Migration guide 0.23.0 → 0.30.0](https://docs.zenml.io/how-to/manage-zenml-server/migration-guide/migration-zero-thirty): How to migrate from ZenML 0.20.0-0.23.0 to 0.30.0-0.39.1.
- [Migration guide 0.39.1 → 0.41.0](https://docs.zenml.io/how-to/manage-zenml-server/migration-guide/migration-zero-forty): How to migrate your ZenML pipelines and steps from version <=0.39.1 to 0.41.0.
- [Migration guide 0.58.2 → 0.60.0](https://docs.zenml.io/how-to/manage-zenml-server/migration-guide/migration-zero-sixty): How to migrate from ZenML 0.58.2 to 0.60.0 (Pydantic 2 edition).
- [Project Setup and Management](https://docs.zenml.io/how-to/project-setup-and-management)
- [Set up a ZenML project](https://docs.zenml.io/how-to/project-setup-and-management/setting-up-a-project-repository): Setting your team up for success with a well-architected ZenML project.
- [Set up a repository](https://docs.zenml.io/how-to/project-setup-and-management/setting-up-a-project-repository/set-up-repository): Recommended repository structure and best practices.
- [Connect your git repository](https://docs.zenml.io/how-to/project-setup-and-management/setting-up-a-project-repository/connect-your-git-repository): Tracking your code and avoiding unnecessary Docker builds by connecting your git repo.
- [Collaborate with your team](https://docs.zenml.io/how-to/project-setup-and-management/collaborate-with-team)
- [Project templates](https://docs.zenml.io/how-to/project-setup-and-management/collaborate-with-team/project-templates): Rocketstart your ZenML journey!
- [Create your own template](https://docs.zenml.io/how-to/project-setup-and-management/collaborate-with-team/project-templates/create-your-own-template): How to create your own ZenML template.
- [Shared components for teams](https://docs.zenml.io/how-to/project-setup-and-management/collaborate-with-team/shared-components-for-teams): Sharing code and libraries within teams.
- [Setting up Stacks, pipelines and models](https://docs.zenml.io/how-to/project-setup-and-management/collaborate-with-team/stacks-pipelines-models): A guide on how to organize stacks, pipelines, models, and artifacts in ZenML.
- [Access management](https://docs.zenml.io/how-to/project-setup-and-management/collaborate-with-team/access-management): A guide on managing user roles and responsibilities in ZenML.
- [Interact with secrets](https://docs.zenml.io/how-to/project-setup-and-management/interact-with-secrets): Registering and using secrets.
- [Pipeline Development](https://docs.zenml.io/how-to/pipeline-development)
- [Build a pipeline](https://docs.zenml.io/how-to/pipeline-development/build-pipelines): Building pipelines is as simple as adding the @step and @pipeline decorators to your code.
- [Use pipeline/step parameters](https://docs.zenml.io/how-to/pipeline-development/build-pipelines/use-pipeline-step-parameters): Steps and pipelines can be parameterized just like any other python function that you are familiar with.
- [Configuring a pipeline at runtime](https://docs.zenml.io/how-to/pipeline-development/build-pipelines/configuring-a-pipeline-at-runtime): Configuring a pipeline at runtime.
- [Reference environment variables in configurations](https://docs.zenml.io/how-to/pipeline-development/build-pipelines/reference-environment-variables-in-configurations)
- [Step output typing and annotation](https://docs.zenml.io/how-to/pipeline-development/build-pipelines/step-output-typing-and-annotation): Step outputs are stored in your artifact store. Annotate and name them to make more explicit.
- [Control caching behavior](https://docs.zenml.io/how-to/pipeline-development/build-pipelines/control-caching-behavior): By default steps in ZenML pipelines are cached whenever code and parameters stay unchanged.
- [Schedule a pipeline](https://docs.zenml.io/how-to/pipeline-development/build-pipelines/schedule-a-pipeline): Learn how to set, pause and stop a schedule for pipelines.
- [Deleting a pipeline](https://docs.zenml.io/how-to/pipeline-development/build-pipelines/delete-a-pipeline): Learn how to delete pipelines.
- [Compose pipelines](https://docs.zenml.io/how-to/pipeline-development/build-pipelines/compose-pipelines): Reuse steps between pipelines.
- [Automatically retry steps](https://docs.zenml.io/how-to/pipeline-development/build-pipelines/retry-steps): Automatically configure your steps to retry if they fail.
- [Run pipelines asynchronously](https://docs.zenml.io/how-to/pipeline-development/build-pipelines/run-pipelines-asynchronously): The best way to trigger a pipeline run so that it runs in the background
- [Control execution order of steps](https://docs.zenml.io/how-to/pipeline-development/build-pipelines/control-execution-order-of-steps)
- [Using a custom step invocation ID](https://docs.zenml.io/how-to/pipeline-development/build-pipelines/using-a-custom-step-invocation-id)
- [Name your pipeline runs](https://docs.zenml.io/how-to/pipeline-development/build-pipelines/name-your-pipeline-runs)
- [Tag your pipeline runs](https://docs.zenml.io/how-to/pipeline-development/build-pipelines/tag-your-pipeline-runs)
- [Use failure/success hooks](https://docs.zenml.io/how-to/pipeline-development/build-pipelines/use-failure-success-hooks): Running failure and success hooks after step execution.
- [Fan in, fan out](https://docs.zenml.io/how-to/pipeline-development/build-pipelines/fan-in-fan-out): Running steps in parallel.
- [Hyperparameter tuning](https://docs.zenml.io/how-to/pipeline-development/build-pipelines/hyper-parameter-tuning): Running a hyperparameter tuning trial with ZenML.
- [Access secrets in a step](https://docs.zenml.io/how-to/pipeline-development/build-pipelines/access-secrets-in-a-step)
- [Run an individual step](https://docs.zenml.io/how-to/pipeline-development/build-pipelines/run-an-individual-step)
- [Fetching pipelines](https://docs.zenml.io/how-to/pipeline-development/build-pipelines/fetching-pipelines): Inspecting a finished pipeline run and its outputs.
- [Get past pipeline/step runs](https://docs.zenml.io/how-to/pipeline-development/build-pipelines/get-past-pipeline-step-runs)
- [Develop locally](https://docs.zenml.io/how-to/pipeline-development/develop-locally): Learn how to develop your pipelines locally.
- [Use config files to develop locally](https://docs.zenml.io/how-to/pipeline-development/develop-locally/local-prod-pipeline-variants): Create different variants of your pipeline for local development and production.
- [Keep your pipelines and dashboard clean](https://docs.zenml.io/how-to/pipeline-development/develop-locally/keep-your-dashboard-server-clean): Learn how to keep your pipeline runs clean during development.
- [Use configuration files](https://docs.zenml.io/how-to/pipeline-development/use-configuration-files): ZenML makes it easy to configure and run a pipeline with configuration files.
- [How to configure a pipeline with a YAML](https://docs.zenml.io/how-to/pipeline-development/use-configuration-files/how-to-use-config): Specify a configuration file
- [What can be configured](https://docs.zenml.io/how-to/pipeline-development/use-configuration-files/what-can-be-configured)
- [Runtime settings for Docker, resources, and stack components](https://docs.zenml.io/how-to/pipeline-development/use-configuration-files/runtime-configuration): Using settings to configure runtime configuration.
- [Configuration hierarchy](https://docs.zenml.io/how-to/pipeline-development/use-configuration-files/configuration-hierarchy): When things can be configured on the pipeline and step level, the step configuration overrides the pipeline.
- [Find out which configuration was used for a run](https://docs.zenml.io/how-to/pipeline-development/use-configuration-files/retrieve-used-configuration-of-a-run)
- [Autogenerate a template yaml file](https://docs.zenml.io/how-to/pipeline-development/use-configuration-files/autogenerate-a-template-yaml-file): To help you figure out what you can put in your configuration file, simply autogenerate a template.
- [Train with GPUs](https://docs.zenml.io/how-to/pipeline-development/training-with-gpus): Ensuring your pipelines or steps run on GPU-backed hardware.
- [Distributed Training with 🤗 Accelerate](https://docs.zenml.io/how-to/pipeline-development/training-with-gpus/accelerate-distributed-training): Run distributed training with Hugging Face's Accelerate library in ZenML pipelines.
- [Run remote pipelines from notebooks](https://docs.zenml.io/how-to/pipeline-development/run-remote-notebooks): Use Jupyter Notebooks to run remote steps or pipelines
- [Limitations of defining steps in notebook cells](https://docs.zenml.io/how-to/pipeline-development/run-remote-notebooks/limitations-of-defining-steps-in-notebook-cells)
- [Run a single step from a notebook](https://docs.zenml.io/how-to/pipeline-development/run-remote-notebooks/run-a-single-step-from-a-notebook)
- [Configure Python environments](https://docs.zenml.io/how-to/pipeline-development/configure-python-environments): Navigating multiple development environments.
- [Handling dependencies](https://docs.zenml.io/how-to/pipeline-development/configure-python-environments/handling-dependencies): How to handle issues with conflicting dependencies
- [Configure the server environment](https://docs.zenml.io/how-to/pipeline-development/configure-python-environments/configure-the-server-environment): How to configure the server environment
- [Trigger a pipeline](https://docs.zenml.io/how-to/trigger-pipelines): There are numerous ways to trigger a pipeline
- [Use templates: Python SDK](https://docs.zenml.io/how-to/trigger-pipelines/use-templates-python): Create and run a template using the ZenML Python SDK
- [Use templates: CLI](https://docs.zenml.io/how-to/trigger-pipelines/use-templates-cli): Create a template using the ZenML CLI
- [Use templates: Dashboard](https://docs.zenml.io/how-to/trigger-pipelines/use-templates-dashboard): Create and run a template over the ZenML Dashboard
- [Use templates: Rest API](https://docs.zenml.io/how-to/trigger-pipelines/use-templates-rest-api): Create and run a template over the ZenML Rest API
- [Customize Docker builds](https://docs.zenml.io/how-to/customize-docker-builds): Using Docker images to run your pipeline.
- [Docker settings on a pipeline](https://docs.zenml.io/how-to/customize-docker-builds/docker-settings-on-a-pipeline): Using Docker images to run your pipeline.
- [Docker settings on a step](https://docs.zenml.io/how-to/customize-docker-builds/docker-settings-on-a-step): You have the option to customize the Docker settings at a step level.
- [Use a prebuilt image for pipeline execution](https://docs.zenml.io/how-to/customize-docker-builds/use-a-prebuilt-image): Skip building an image for your ZenML pipeline altogether.
- [Specify pip dependencies and apt packages](https://docs.zenml.io/how-to/customize-docker-builds/specify-pip-dependencies-and-apt-packages)
- [How to use a private PyPI repository](https://docs.zenml.io/how-to/customize-docker-builds/how-to-use-a-private-pypi-repository): How to use a private PyPI repository.
- [Use your own Dockerfiles](https://docs.zenml.io/how-to/customize-docker-builds/use-your-own-docker-files)
- [Which files are built into the image](https://docs.zenml.io/how-to/customize-docker-builds/which-files-are-built-into-the-image)
- [How to reuse builds](https://docs.zenml.io/how-to/customize-docker-builds/how-to-reuse-builds): Learn how to reuse builds to speed up your pipeline runs.
- [Define where an image is built](https://docs.zenml.io/how-to/customize-docker-builds/define-where-an-image-is-built): Defining the image builder.
- [Data and Artifact Management](https://docs.zenml.io/how-to/data-artifact-management)
- [Understand ZenML artifacts](https://docs.zenml.io/how-to/data-artifact-management/handle-data-artifacts): Step outputs in ZenML are stored in the artifact store. This enables caching, lineage and auditability. Using type annotations helps with transparency,  passing data between steps, and serializing/des
- [How ZenML stores data](https://docs.zenml.io/how-to/data-artifact-management/handle-data-artifacts/artifact-versioning): Understand how ZenML stores your data under-the-hood.
- [Return multiple outputs from a step](https://docs.zenml.io/how-to/data-artifact-management/handle-data-artifacts/return-multiple-outputs-from-a-step): Use Annotated to return multiple outputs from a step and name them for easy retrieval and dashboard display.
- [Delete an artifact](https://docs.zenml.io/how-to/data-artifact-management/handle-data-artifacts/delete-an-artifact): Learn how to delete artifacts.
- [Artifacts naming](https://docs.zenml.io/how-to/data-artifact-management/handle-data-artifacts/artifacts-naming): Understand how you can name your ZenML artifacts.
- [Organize data with tags](https://docs.zenml.io/how-to/data-artifact-management/handle-data-artifacts/tagging): Use tags to organize tags in ZenML.
- [Get arbitrary artifacts in a step](https://docs.zenml.io/how-to/data-artifact-management/handle-data-artifacts/get-arbitrary-artifacts-in-a-step): Not all artifacts need to come through the step interface from direct upstream steps.
- [Handle custom data types](https://docs.zenml.io/how-to/data-artifact-management/handle-data-artifacts/handle-custom-data-types): Using materializers to pass custom data types through steps.
- [Load artifacts into memory](https://docs.zenml.io/how-to/data-artifact-management/handle-data-artifacts/load-artifacts-into-memory)
- [Complex use-cases](https://docs.zenml.io/how-to/data-artifact-management/complex-usecases)
- [Datasets in ZenML](https://docs.zenml.io/how-to/data-artifact-management/complex-usecases/datasets): Model datasets using simple abstractions.
- [Manage big data](https://docs.zenml.io/how-to/data-artifact-management/complex-usecases/manage-big-data): Learn about how to manage big data with ZenML.
- [Skipping materialization](https://docs.zenml.io/how-to/data-artifact-management/complex-usecases/unmaterialized-artifacts): Skip materialization of artifacts.
- [Passing artifacts between pipelines](https://docs.zenml.io/how-to/data-artifact-management/complex-usecases/passing-artifacts-between-pipelines): Structuring an MLOps project
- [Register Existing Data as a ZenML Artifact](https://docs.zenml.io/how-to/data-artifact-management/complex-usecases/registering-existing-data): Learn how to register an external data as a ZenML artifact for future use.
- [Visualizing artifacts](https://docs.zenml.io/how-to/data-artifact-management/visualize-artifacts): Configuring ZenML to display data visualizations in the dashboard.
- [Default visualizations](https://docs.zenml.io/how-to/data-artifact-management/visualize-artifacts/types-of-visualizations): Types of visualizations in ZenML.
- [Creating custom visualizations](https://docs.zenml.io/how-to/data-artifact-management/visualize-artifacts/creating-custom-visualizations): Creating your own visualizations.
- [Displaying visualizations in the dashboard](https://docs.zenml.io/how-to/data-artifact-management/visualize-artifacts/visualizations-in-dashboard): Displaying visualizations in the dashboard.
- [Disabling visualizations](https://docs.zenml.io/how-to/data-artifact-management/visualize-artifacts/disabling-visualizations): Disabling visualizations.
- [Model Management and Metrics](https://docs.zenml.io/how-to/model-management-metrics)
- [Use the Model Control Plane](https://docs.zenml.io/how-to/model-management-metrics/model-control-plane)
- [Registering a Model](https://docs.zenml.io/how-to/model-management-metrics/model-control-plane/register-a-model)
- [Deleting a Model](https://docs.zenml.io/how-to/model-management-metrics/model-control-plane/delete-a-model): Learn how to delete models.
- [Associate a pipeline with a Model](https://docs.zenml.io/how-to/model-management-metrics/model-control-plane/associate-a-pipeline-with-a-model)
- [Connecting artifacts via a Model](https://docs.zenml.io/how-to/model-management-metrics/model-control-plane/connecting-artifacts-via-a-model): Structuring an MLOps project
- [Controlling Model versions](https://docs.zenml.io/how-to/model-management-metrics/model-control-plane/model-versions)
- [Load a Model in code](https://docs.zenml.io/how-to/model-management-metrics/model-control-plane/load-a-model-in-code)
- [Promote a Model](https://docs.zenml.io/how-to/model-management-metrics/model-control-plane/promote-a-model)
- [Linking model binaries/data to a Model](https://docs.zenml.io/how-to/model-management-metrics/model-control-plane/linking-model-binaries-data-to-models)
- [Load artifacts from Model](https://docs.zenml.io/how-to/model-management-metrics/model-control-plane/load-artifacts-from-model)
- [Track metrics and metadata](https://docs.zenml.io/how-to/model-management-metrics/track-metrics-metadata): Tracking and comparing metrics and metadata
- [Attach metadata to a step](https://docs.zenml.io/how-to/model-management-metrics/track-metrics-metadata/attach-metadata-to-a-step): Learn how to attach metadata to a step.
- [Attach metadata to a run](https://docs.zenml.io/how-to/model-management-metrics/track-metrics-metadata/attach-metadata-to-a-run): Learn how to attach metadata to a run.
- [Attach metadata to an artifact](https://docs.zenml.io/how-to/model-management-metrics/track-metrics-metadata/attach-metadata-to-an-artifact): Learn how to attach metadata to an artifact.
- [Attach metadata to a model](https://docs.zenml.io/how-to/model-management-metrics/track-metrics-metadata/attach-metadata-to-a-model): Learn how to attach metadata to a model.
- [Group metadata](https://docs.zenml.io/how-to/model-management-metrics/track-metrics-metadata/grouping-metadata): Learn how to group key-value pairs in the dashboard.
- [Special Metadata Types](https://docs.zenml.io/how-to/model-management-metrics/track-metrics-metadata/logging-metadata): Tracking your metadata.
- [Fetch metadata within steps](https://docs.zenml.io/how-to/model-management-metrics/track-metrics-metadata/fetch-metadata-within-steps): Accessing meta information in real-time within your pipeline.
- [Fetch metadata during pipeline composition](https://docs.zenml.io/how-to/model-management-metrics/track-metrics-metadata/fetch-metadata-within-pipeline): How to fetch metadata during pipeline composition.
- [Stack infrastructure and deployment](https://docs.zenml.io/how-to/infrastructure-deployment)
- [Manage stacks & components](https://docs.zenml.io/how-to/infrastructure-deployment/stack-deployment): Stacks represent the infrastructure and tooling that defines where and how a pipeline executes.
- [Deploy a cloud stack with ZenML](https://docs.zenml.io/how-to/infrastructure-deployment/stack-deployment/deploy-a-cloud-stack): Deploy a cloud stack from scratch with a single click
- [Deploy a cloud stack with Terraform](https://docs.zenml.io/how-to/infrastructure-deployment/stack-deployment/deploy-a-cloud-stack-with-terraform): Deploy a cloud stack using Terraform
- [Register a cloud stack](https://docs.zenml.io/how-to/infrastructure-deployment/stack-deployment/register-a-cloud-stack): Seamlessly register a cloud stack by using existing infrastructure
- [Export and install stack requirements](https://docs.zenml.io/how-to/infrastructure-deployment/stack-deployment/export-stack-requirements): Export stack requirements
- [Reference secrets in stack configuration](https://docs.zenml.io/how-to/infrastructure-deployment/stack-deployment/reference-secrets-in-stack-configuration): Reference secrets in stack component attributes and settings
- [Implement a custom stack component](https://docs.zenml.io/how-to/infrastructure-deployment/stack-deployment/implement-a-custom-stack-component): How to write a custom stack component flavor
- [Infrastructure as code](https://docs.zenml.io/how-to/infrastructure-deployment/infrastructure-as-code): Leverage Infrastructure as Code to manage your ZenML stacks and components.
- [Manage your stacks with Terraform](https://docs.zenml.io/how-to/infrastructure-deployment/infrastructure-as-code/terraform-stack-management): Registering Existing Infrastructure with ZenML - A Guide for Terraform Users
- [ZenML & Terraform Best Practices](https://docs.zenml.io/how-to/infrastructure-deployment/infrastructure-as-code/best-practices): Best practices for using IaC with ZenML
- [Connect services via connectors](https://docs.zenml.io/how-to/infrastructure-deployment/auth-management): Connect your ZenML deployment to a cloud provider and other infrastructure services and resources.
- [Service Connectors guide](https://docs.zenml.io/how-to/infrastructure-deployment/auth-management/service-connectors-guide): The complete guide to managing Service Connectors and connecting ZenML to external resources.
- [Security best practices](https://docs.zenml.io/how-to/infrastructure-deployment/auth-management/best-security-practices): Best practices concerning the various authentication methods implemented by Service Connectors.
- [Docker Service Connector](https://docs.zenml.io/how-to/infrastructure-deployment/auth-management/docker-service-connector): Configuring Docker Service Connectors to connect ZenML to Docker container registries.
- [Kubernetes Service Connector](https://docs.zenml.io/how-to/infrastructure-deployment/auth-management/kubernetes-service-connector): Configuring Kubernetes Service Connectors to connect ZenML to Kubernetes clusters.
- [AWS Service Connector](https://docs.zenml.io/how-to/infrastructure-deployment/auth-management/aws-service-connector): Configuring AWS Service Connectors to connect ZenML to AWS resources like S3 buckets, EKS Kubernetes clusters and ECR container registries.
- [GCP Service Connector](https://docs.zenml.io/how-to/infrastructure-deployment/auth-management/gcp-service-connector): Configuring GCP Service Connectors to connect ZenML to GCP resources such as GCS buckets, GKE Kubernetes clusters, and GCR container registries.
- [Azure Service Connector](https://docs.zenml.io/how-to/infrastructure-deployment/auth-management/azure-service-connector): Configuring Azure Service Connectors to connect ZenML to Azure resources such as Blob storage buckets, AKS Kubernetes clusters, and ACR container registries.
- [HyperAI Service Connector](https://docs.zenml.io/how-to/infrastructure-deployment/auth-management/hyperai-service-connector): Configuring HyperAI Connectors to connect ZenML to HyperAI instances.
- [Control logging](https://docs.zenml.io/how-to/control-logging): Configuring ZenML's default logging behavior
- [View logs on the dashboard](https://docs.zenml.io/how-to/control-logging/view-logs-on-the-dasbhoard)
- [Enable or disable logs storage](https://docs.zenml.io/how-to/control-logging/enable-or-disable-logs-storing)
- [Set logging verbosity](https://docs.zenml.io/how-to/control-logging/set-logging-verbosity): How to set the logging verbosity in ZenML.
- [Set logging format](https://docs.zenml.io/how-to/control-logging/set-logging-format): How to set the logging format in ZenML.
- [Disable rich traceback output](https://docs.zenml.io/how-to/control-logging/disable-rich-traceback): How to disable rich traceback output in ZenML.
- [Disable colorful logging](https://docs.zenml.io/how-to/control-logging/disable-colorful-logging): How to disable colorful logging in ZenML.
- [Popular integrations](https://docs.zenml.io/how-to/popular-integrations): Use your favorite tools with ZenML.
- [Run on AWS](https://docs.zenml.io/how-to/popular-integrations/aws-guide): A simple guide to create an AWS stack to run your ZenML pipelines
- [Run on GCP](https://docs.zenml.io/how-to/popular-integrations/gcp-guide): A simple guide to quickly set up a minimal stack on GCP.
- [Run on Azure](https://docs.zenml.io/how-to/popular-integrations/azure-guide): A simple guide to create an Azure stack to run your ZenML pipelines
- [Kubeflow](https://docs.zenml.io/how-to/popular-integrations/kubeflow): Run your ML pipelines on Kubeflow Pipelines.
- [Kubernetes](https://docs.zenml.io/how-to/popular-integrations/kubernetes): Learn how to deploy ZenML pipelines on a Kubernetes cluster.
- [MLflow](https://docs.zenml.io/how-to/popular-integrations/mlflow): Learn how to use the MLflow Experiment Tracker with ZenML.
- [Skypilot](https://docs.zenml.io/how-to/popular-integrations/skypilot): Use Skypilot with ZenML.
- [Contribute to/Extend ZenML](https://docs.zenml.io/how-to/contribute-to-zenml): Contributing to ZenML.
- [Implement a custom integration](https://docs.zenml.io/how-to/contribute-to-zenml/implement-a-custom-integration): Creating an external integration and contributing to ZenML
- [Debug and solve issues](https://docs.zenml.io/how-to/debug-and-solve-issues): A guide to debug common issues and get help.
- [llms.txt](https://docs.zenml.io/reference/llms-txt): The llms.txt file(s) for ZenML
- [Python Client](https://docs.zenml.io/reference/python-client): Interacting with your ZenML instance through the ZenML Client.
- [Global settings](https://docs.zenml.io/reference/global-settings): Understanding the global settings of your ZenML installation.
- [Environment Variables](https://docs.zenml.io/reference/environment-variables): How to control ZenML behavior with environmental variables.
- [How do I...?](https://docs.zenml.io/reference/how-do-i): Links to common use cases, workflows and tasks using ZenML.
- [Community & content](https://docs.zenml.io/reference/community-and-content): All possible ways for our community to get in touch with ZenML.
- [FAQ](https://docs.zenml.io/reference/faq): Find answers to the most frequently asked questions about ZenML.
- [Legacy docs](https://docs.zenml.io/reference/legacy-docs): All legacy docs of ZenML

## Learn

- [Learn ZenML](https://docs.zenml.io/user-guides/readme): Guides, examples and projects
- [Starter guide](https://docs.zenml.io/user-guides/starter-guide): Kickstart your journey into MLOps with the essentials of ZenML.
- [Create an ML pipeline](https://docs.zenml.io/user-guides/starter-guide/create-an-ml-pipeline): Start with the basics of steps and pipelines.
- [Cache previous executions](https://docs.zenml.io/user-guides/starter-guide/cache-previous-executions): Iterating quickly with ZenML through caching.
- [Manage artifacts](https://docs.zenml.io/user-guides/starter-guide/manage-artifacts): Understand and adjust how ZenML versions your data.
- [Track ML models](https://docs.zenml.io/user-guides/starter-guide/track-ml-models): Creating a full picture of a ML model using the Model Control Plane
- [A starter project](https://docs.zenml.io/user-guides/starter-guide/starter-project): Put your new knowledge into action with a simple starter project
- [Production guide](https://docs.zenml.io/user-guides/production-guide): Level up your skills in a production setting.
- [Deploying ZenML](https://docs.zenml.io/user-guides/production-guide/deploying-zenml): Deploying ZenML is the first step to production.
- [Understanding stacks](https://docs.zenml.io/user-guides/production-guide/understand-stacks): Learning how to switch the infrastructure backend of your code.
- [Connecting remote storage](https://docs.zenml.io/user-guides/production-guide/remote-storage): Transitioning to remote artifact storage.
- [Orchestrate on the cloud](https://docs.zenml.io/user-guides/production-guide/cloud-orchestration): Orchestrate using cloud resources.
- [Configure your pipeline to add compute](https://docs.zenml.io/user-guides/production-guide/configure-pipeline): Add more resources to your pipeline configuration.
- [Configure a code repository](https://docs.zenml.io/user-guides/production-guide/connect-code-repository): Connect a Git repository to ZenML to track code changes and collaborate on MLOps projects.
- [Set up CI/CD](https://docs.zenml.io/user-guides/production-guide/ci-cd): Managing the lifecycle of a ZenML pipeline with Continuous Integration and Delivery
- [An end-to-end project](https://docs.zenml.io/user-guides/production-guide/end-to-end): Put your new knowledge in action with an end-to-end project
- [LLMOps guide](https://docs.zenml.io/user-guides/llmops-guide): Leverage the power of LLMs in your MLOps workflows with ZenML.
- [RAG with ZenML](https://docs.zenml.io/user-guides/llmops-guide/rag-with-zenml): RAG is a sensible way to get started with LLMs.
- [RAG in 85 lines of code](https://docs.zenml.io/user-guides/llmops-guide/rag-with-zenml/rag-85-loc): Learn how to implement a RAG pipeline in just 85 lines of code.
- [Understanding Retrieval-Augmented Generation (RAG)](https://docs.zenml.io/user-guides/llmops-guide/rag-with-zenml/understanding-rag): Understand the Retrieval-Augmented Generation (RAG) technique and its benefits.
- [Data ingestion and preprocessing](https://docs.zenml.io/user-guides/llmops-guide/rag-with-zenml/data-ingestion): Understand how to ingest and preprocess data for RAG pipelines with ZenML.
- [Embeddings generation](https://docs.zenml.io/user-guides/llmops-guide/rag-with-zenml/embeddings-generation): Generate embeddings to improve retrieval performance.
- [Storing embeddings in a vector database](https://docs.zenml.io/user-guides/llmops-guide/rag-with-zenml/storing-embeddings-in-a-vector-database): Store embeddings in a vector database for efficient retrieval.
- [Basic RAG inference pipeline](https://docs.zenml.io/user-guides/llmops-guide/rag-with-zenml/basic-rag-inference-pipeline): Use your RAG components to generate responses to prompts.
- [Evaluation and metrics](https://docs.zenml.io/user-guides/llmops-guide/evaluation): Track how your RAG pipeline improves using evaluation and metrics.
- [Evaluation in 65 lines of code](https://docs.zenml.io/user-guides/llmops-guide/evaluation/evaluation-in-65-loc): Learn how to implement evaluation for RAG in just 65 lines of code.
- [Retrieval evaluation](https://docs.zenml.io/user-guides/llmops-guide/evaluation/retrieval): See how the retrieval component responds to changes in the pipeline.
- [Generation evaluation](https://docs.zenml.io/user-guides/llmops-guide/evaluation/generation): Evaluate the generation component of your RAG pipeline.
- [Evaluation in practice](https://docs.zenml.io/user-guides/llmops-guide/evaluation/evaluation-in-practice): Learn how to evaluate the performance of your RAG system in practice.
- [Reranking for better retrieval](https://docs.zenml.io/user-guides/llmops-guide/reranking): Add reranking to your RAG inference for better retrieval performance.
- [Understanding reranking](https://docs.zenml.io/user-guides/llmops-guide/reranking/understanding-reranking): Understand how reranking works.
- [Implementing reranking in ZenML](https://docs.zenml.io/user-guides/llmops-guide/reranking/implementing-reranking): Learn how to implement reranking in ZenML.
- [Evaluating reranking performance](https://docs.zenml.io/user-guides/llmops-guide/reranking/evaluating-reranking-performance): Evaluate the performance of your reranking model.
- [Improve retrieval by finetuning embeddings](https://docs.zenml.io/user-guides/llmops-guide/finetuning-embeddings): Finetune embeddings on custom synthetic data to improve retrieval performance.
- [Synthetic data generation](https://docs.zenml.io/user-guides/llmops-guide/finetuning-embeddings/synthetic-data-generation): Generate synthetic data with distilabel to finetune embeddings.
- [Finetuning embeddings with Sentence Transformers](https://docs.zenml.io/user-guides/llmops-guide/finetuning-embeddings/finetuning-embeddings-with-sentence-transformers): Finetune embeddings with Sentence Transformers.
- [Evaluating finetuned embeddings](https://docs.zenml.io/user-guides/llmops-guide/finetuning-embeddings/evaluating-finetuned-embeddings): Evaluate finetuned embeddings and compare to original base embeddings.
- [Finetuning LLMs with ZenML](https://docs.zenml.io/user-guides/llmops-guide/finetuning-llms): Finetune LLMs for specific tasks or to improve performance and cost.
- [Finetuning in 100 lines of code](https://docs.zenml.io/user-guides/llmops-guide/finetuning-llms/finetuning-100-loc): Learn how to implement an LLM fine-tuning pipeline in just 100 lines of code.
- [Why and when to finetune LLMs](https://docs.zenml.io/user-guides/llmops-guide/finetuning-llms/why-and-when-to-finetune-llms): Deciding when is the right time to finetune LLMs.
- [Starter choices with finetuning](https://docs.zenml.io/user-guides/llmops-guide/finetuning-llms/starter-choices-for-finetuning-llms): Get started with finetuning LLMs by picking a use case and data.
- [Finetuning with 🤗 Accelerate](https://docs.zenml.io/user-guides/llmops-guide/finetuning-llms/finetuning-with-accelerate): Finetuning an LLM with Accelerate and PEFT
- [Evaluation for finetuning](https://docs.zenml.io/user-guides/llmops-guide/finetuning-llms/evaluation-for-finetuning)
- [Deploying finetuned models](https://docs.zenml.io/user-guides/llmops-guide/finetuning-llms/deploying-finetuned-models)
- [Next steps](https://docs.zenml.io/user-guides/llmops-guide/finetuning-llms/next-steps)
- [Organizing pipelines and models](https://docs.zenml.io/user-guides/tutorial/organizing-pipelines-and-models): A step-by-step tutorial on effectively organizing your ML assets in ZenML using tags and projects
- [Managing scheduled pipelines](https://docs.zenml.io/user-guides/tutorial/managing-scheduled-pipelines): A step-by-step tutorial on how to create, update, and delete scheduled   pipelines in ZenML

## ZenML Pro

- [Introduction](https://docs.zenml.io/pro/readme): Learn about the ZenML Pro features and deployment scenarios.
- [Self-hosted deployment](https://docs.zenml.io/pro/deployments/self-hosted): Guide for installing ZenML Pro self-hosted in a Kubernetes cluster.
- [Hierarchy](https://docs.zenml.io/pro/core-concepts/hierarchy): Understanding ZenML's hierarchical structure
- [Organizations](https://docs.zenml.io/pro/core-concepts/organization): Manage organizations in ZenML
- [Workspaces](https://docs.zenml.io/pro/core-concepts/workspaces): Learn how to use workspaces in ZenML Pro.
- [Projects](https://docs.zenml.io/pro/core-concepts/projects): Managing projects in ZenML
- [Teams](https://docs.zenml.io/pro/core-concepts/teams): Learn about Teams in ZenML Pro and how they can be used to manage groups of users across your organization and workspaces.
- [Roles & Permissions](https://docs.zenml.io/pro/core-concepts/roles): Learn about the different roles and permissions you can assign to your team members in ZenML Pro.

## Stacks

- [Overview](https://docs.zenml.io/stacks/component-guide): Overview of categories of MLOps components and third-party integrations.
- [Integrations](https://docs.zenml.io/stacks/integrations)
- [Orchestrators](https://docs.zenml.io/stacks/orchestrators): Orchestrating the execution of ML pipelines.
- [Local Orchestrator](https://docs.zenml.io/stacks/orchestrators/local): Orchestrating your pipelines to run locally.
- [Local Docker Orchestrator](https://docs.zenml.io/stacks/orchestrators/local-docker): Orchestrating your pipelines to run in Docker.
- [Kubeflow Orchestrator](https://docs.zenml.io/stacks/orchestrators/kubeflow): Orchestrating your pipelines to run on Kubeflow.
- [Kubernetes Orchestrator](https://docs.zenml.io/stacks/orchestrators/kubernetes): Orchestrating your pipelines to run on Kubernetes clusters.
- [Google Cloud VertexAI Orchestrator](https://docs.zenml.io/stacks/orchestrators/vertex): Orchestrating your pipelines to run on Vertex AI.
- [AWS Sagemaker Orchestrator](https://docs.zenml.io/stacks/orchestrators/sagemaker): Orchestrating your pipelines to run on Amazon Sagemaker.
- [AzureML Orchestrator](https://docs.zenml.io/stacks/orchestrators/azureml): Orchestrating your pipelines to run on AzureML.
- [Databricks Orchestrator](https://docs.zenml.io/stacks/orchestrators/databricks): Orchestrating your pipelines to run on Databricks.
- [Tekton Orchestrator](https://docs.zenml.io/stacks/orchestrators/tekton): Orchestrating your pipelines to run on Tekton.
- [Airflow Orchestrator](https://docs.zenml.io/stacks/orchestrators/airflow): Orchestrating your pipelines to run on Airflow.
- [Skypilot VM Orchestrator](https://docs.zenml.io/stacks/orchestrators/skypilot-vm): Orchestrating your pipelines to run on VMs using SkyPilot.
- [HyperAI Orchestrator](https://docs.zenml.io/stacks/orchestrators/hyperai): Orchestrating your pipelines to run on HyperAI.ai instances.
- [Lightning AI Orchestrator](https://docs.zenml.io/stacks/orchestrators/lightning): Orchestrating your pipelines to run on Lightning AI.
- [Develop a custom orchestrator](https://docs.zenml.io/stacks/orchestrators/custom): Learning how to develop a custom orchestrator.
- [Artifact Stores](https://docs.zenml.io/stacks/artifact-stores): Setting up a persistent storage for your artifacts.
- [Local Artifact Store](https://docs.zenml.io/stacks/artifact-stores/local): Storing artifacts on your local filesystem.
- [Amazon Simple Cloud Storage (S3)](https://docs.zenml.io/stacks/artifact-stores/s3): Storing artifacts in an AWS S3 bucket.
- [Google Cloud Storage (GCS)](https://docs.zenml.io/stacks/artifact-stores/gcp): Storing artifacts using GCP Cloud Storage.
- [Azure Blob Storage](https://docs.zenml.io/stacks/artifact-stores/azure): Storing artifacts using Azure Blob Storage
- [Develop a custom artifact store](https://docs.zenml.io/stacks/artifact-stores/custom): Learning how to develop a custom artifact store.
- [Container Registries](https://docs.zenml.io/stacks/container-registries): Setting up a storage for Docker images.
- [Default Container Registry](https://docs.zenml.io/stacks/container-registries/default): Storing container images locally.
- [DockerHub](https://docs.zenml.io/stacks/container-registries/dockerhub): Storing container images in DockerHub.
- [Amazon Elastic Container Registry (ECR)](https://docs.zenml.io/stacks/container-registries/aws): Storing container images in Amazon ECR.
- [Google Cloud Container Registry](https://docs.zenml.io/stacks/container-registries/gcp): Storing container images in GCP.
- [Azure Container Registry](https://docs.zenml.io/stacks/container-registries/azure): Storing container images in Azure.
- [GitHub Container Registry](https://docs.zenml.io/stacks/container-registries/github): Storing container images in GitHub.
- [Develop a custom container registry](https://docs.zenml.io/stacks/container-registries/custom): Learning how to develop a custom container registry.
- [Step Operators](https://docs.zenml.io/stacks/step-operators): Executing individual steps in specialized environments.
- [Amazon SageMaker](https://docs.zenml.io/stacks/step-operators/sagemaker): Executing individual steps in SageMaker.
- [AzureML](https://docs.zenml.io/stacks/step-operators/azureml): Executing individual steps in AzureML.
- [Google Cloud VertexAI](https://docs.zenml.io/stacks/step-operators/vertex): Executing individual steps in Vertex AI.
- [Kubernetes](https://docs.zenml.io/stacks/step-operators/kubernetes): Executing individual steps in Kubernetes Pods.
- [Modal](https://docs.zenml.io/stacks/step-operators/modal): Executing individual steps in Modal.
- [Spark](https://docs.zenml.io/stacks/step-operators/spark-kubernetes): Executing individual steps on Spark
- [Develop a Custom Step Operator](https://docs.zenml.io/stacks/step-operators/custom): Learning how to develop a custom step operator.
- [Experiment Trackers](https://docs.zenml.io/stacks/experiment-trackers): Logging and visualizing ML experiments.
- [Comet](https://docs.zenml.io/stacks/experiment-trackers/comet): Logging and visualizing experiments with Comet.
- [MLflow](https://docs.zenml.io/stacks/experiment-trackers/mlflow): Logging and visualizing experiments with MLflow.
- [Neptune](https://docs.zenml.io/stacks/experiment-trackers/neptune): Logging and visualizing experiments with neptune.ai
- [Weights & Biases](https://docs.zenml.io/stacks/experiment-trackers/wandb): Logging and visualizing experiments with Weights & Biases.
- [Google Cloud VertexAI Experiment Tracker](https://docs.zenml.io/stacks/experiment-trackers/vertexai): Logging and visualizing experiments with Vertex AI Experiment Tracker.
- [Develop a custom experiment tracker](https://docs.zenml.io/stacks/experiment-trackers/custom): Learning how to develop a custom experiment tracker.
- [Image Builders](https://docs.zenml.io/stacks/image-builders): Building container images for your ML workflow.
- [Local Image Builder](https://docs.zenml.io/stacks/image-builders/local): Building container images locally.
- [Kaniko Image Builder](https://docs.zenml.io/stacks/image-builders/kaniko): Building container images with Kaniko.
- [AWS Image Builder](https://docs.zenml.io/stacks/image-builders/aws): Building container images with AWS CodeBuild
- [Google Cloud Image Builder](https://docs.zenml.io/stacks/image-builders/gcp): Building container images with Google Cloud Build
- [Develop a Custom Image Builder](https://docs.zenml.io/stacks/image-builders/custom): Learning how to develop a custom image builder.
- [Alerters](https://docs.zenml.io/stacks/alerters): Sending automated alerts to chat services.
- [Discord Alerter](https://docs.zenml.io/stacks/alerters/discord): Sending automated alerts to a Discord channel.
- [Slack Alerter](https://docs.zenml.io/stacks/alerters/slack): Sending automated alerts to a Slack channel.
- [Develop a Custom Alerter](https://docs.zenml.io/stacks/alerters/custom): Learning how to develop a custom alerter.
- [Annotators](https://docs.zenml.io/stacks/annotators): Annotating the data in your workflow.
- [Argilla](https://docs.zenml.io/stacks/annotators/argilla): Annotating data using Argilla.
- [Label Studio](https://docs.zenml.io/stacks/annotators/label-studio): Annotating data using Label Studio.
- [Pigeon](https://docs.zenml.io/stacks/annotators/pigeon): Annotating data using Pigeon.
- [Prodigy](https://docs.zenml.io/stacks/annotators/prodigy): Annotating data using Prodigy.
- [Develop a Custom Annotator](https://docs.zenml.io/stacks/annotators/custom): Learning how to develop a custom annotator.
- [Data Validators](https://docs.zenml.io/stacks/data-validators): How to enhance and maintain the quality of your data and the performance of your models with data profiling and validation
- [Great Expectations](https://docs.zenml.io/stacks/data-validators/great-expectations): How to use Great Expectations to run data quality checks in your pipelines and document the results
- [Deepchecks](https://docs.zenml.io/stacks/data-validators/deepchecks): How to test the data and models used in your pipelines with Deepchecks test suites
- [Evidently](https://docs.zenml.io/stacks/data-validators/evidently): How to keep your data quality in check and guard against data and model drift with Evidently profiling
- [Whylogs](https://docs.zenml.io/stacks/data-validators/whylogs): How to collect and visualize statistics to track changes in your pipelines' data with whylogs/WhyLabs profiling.
- [Develop a custom data validator](https://docs.zenml.io/stacks/data-validators/custom): How to develop a custom data validator
- [Feature Stores](https://docs.zenml.io/stacks/feature-stores): Managing data in feature stores.
- [Feast](https://docs.zenml.io/stacks/feature-stores/feast): Managing data in Feast feature stores.
- [Develop a Custom Feature Store](https://docs.zenml.io/stacks/feature-stores/custom): Learning how to develop a custom feature store.
- [Model Deployers](https://docs.zenml.io/stacks/model-deployers): Deploying your models and serve real-time predictions.
- [MLflow](https://docs.zenml.io/stacks/model-deployers/mlflow): Deploying your models locally with MLflow.
- [Seldon](https://docs.zenml.io/stacks/model-deployers/seldon): Deploying models to Kubernetes with Seldon Core.
- [BentoML](https://docs.zenml.io/stacks/model-deployers/bentoml): Deploying your models locally with BentoML.
- [Hugging Face](https://docs.zenml.io/stacks/model-deployers/huggingface): Deploying models to Huggingface Inference Endpoints with Hugging Face :hugging\_face:.
- [Databricks](https://docs.zenml.io/stacks/model-deployers/databricks): Deploying models to Databricks Inference Endpoints with Databricks
- [vLLM](https://docs.zenml.io/stacks/model-deployers/vllm): Deploying your LLM locally with vLLM.
- [Develop a Custom Model Deployer](https://docs.zenml.io/stacks/model-deployers/custom): Learning how to develop a custom model deployer.
- [Model Registries](https://docs.zenml.io/stacks/model-registries): Tracking and managing ML models.
- [MLflow Model Registry](https://docs.zenml.io/stacks/model-registries/mlflow): Managing MLFlow logged models and artifacts
- [Develop a Custom Model Registry](https://docs.zenml.io/stacks/model-registries/custom): Learning how to develop a custom model registry.

## API Reference

- [Overview](https://docs.zenml.io/api-reference/readme): The ZenML API provides programmatic access to ZenML services beyond what's available in the Python SDK.
- [Getting Started](https://docs.zenml.io/api-reference/oss-api/getting-started)
- [OSS API](https://docs.zenml.io/api-reference/oss-api/oss-api)
- [Artifacts](https://docs.zenml.io/api-reference/oss-api/oss-api/artifacts)
- [Artifact versions](https://docs.zenml.io/api-reference/oss-api/oss-api/artifact-versions)
- [Batch](https://docs.zenml.io/api-reference/oss-api/oss-api/artifact-versions/batch)
- [Visualize](https://docs.zenml.io/api-reference/oss-api/oss-api/artifact-versions/visualize)
- [Login](https://docs.zenml.io/api-reference/oss-api/oss-api/login)
- [Logout](https://docs.zenml.io/api-reference/oss-api/oss-api/logout)
- [Device authorization](https://docs.zenml.io/api-reference/oss-api/oss-api/device-authorization)
- [Api token](https://docs.zenml.io/api-reference/oss-api/oss-api/api-token)
- [Code repositories](https://docs.zenml.io/api-reference/oss-api/oss-api/code-repositories)
- [Logs](https://docs.zenml.io/api-reference/oss-api/oss-api/logs)
- [Models](https://docs.zenml.io/api-reference/oss-api/oss-api/models)
- [Model versions](https://docs.zenml.io/api-reference/oss-api/oss-api/models/model-versions)
- [Model versions](https://docs.zenml.io/api-reference/oss-api/oss-api/model-versions)
- [Artifacts](https://docs.zenml.io/api-reference/oss-api/oss-api/model-versions/artifacts)
- [Runs](https://docs.zenml.io/api-reference/oss-api/oss-api/model-versions/runs)
- [Pipelines](https://docs.zenml.io/api-reference/oss-api/oss-api/pipelines)
- [Runs](https://docs.zenml.io/api-reference/oss-api/oss-api/pipelines/runs)
- [Runs](https://docs.zenml.io/api-reference/oss-api/oss-api/runs)
- [Steps](https://docs.zenml.io/api-reference/oss-api/oss-api/runs/steps)
- [Pipeline configuration](https://docs.zenml.io/api-reference/oss-api/oss-api/runs/pipeline-configuration)
- [Status](https://docs.zenml.io/api-reference/oss-api/oss-api/runs/status)
- [Refresh](https://docs.zenml.io/api-reference/oss-api/oss-api/runs/refresh)
- [Run templates](https://docs.zenml.io/api-reference/oss-api/oss-api/run-templates)
- [Runs](https://docs.zenml.io/api-reference/oss-api/oss-api/run-templates/runs)
- [Schedules](https://docs.zenml.io/api-reference/oss-api/oss-api/schedules)
- [Secrets](https://docs.zenml.io/api-reference/oss-api/oss-api/secrets)
- [Info](https://docs.zenml.io/api-reference/oss-api/oss-api/info)
- [Service accounts](https://docs.zenml.io/api-reference/oss-api/oss-api/service-accounts)
- [Api keys](https://docs.zenml.io/api-reference/oss-api/oss-api/service-accounts/api-keys)
- [Rotate](https://docs.zenml.io/api-reference/oss-api/oss-api/service-accounts/rotate)
- [Service connectors](https://docs.zenml.io/api-reference/oss-api/oss-api/service-connectors)
- [Verify](https://docs.zenml.io/api-reference/oss-api/oss-api/service-connectors/verify)
- [Client](https://docs.zenml.io/api-reference/oss-api/oss-api/service-connectors/client)
- [Full stack resources](https://docs.zenml.io/api-reference/oss-api/oss-api/service-connectors/full-stack-resources)
- [Services](https://docs.zenml.io/api-reference/oss-api/oss-api/services)
- [Stacks](https://docs.zenml.io/api-reference/oss-api/oss-api/stacks)
- [Components](https://docs.zenml.io/api-reference/oss-api/oss-api/components)
- [Component types](https://docs.zenml.io/api-reference/oss-api/oss-api/component-types)
- [Steps](https://docs.zenml.io/api-reference/oss-api/oss-api/steps)
- [Step configuration](https://docs.zenml.io/api-reference/oss-api/oss-api/steps/step-configuration)
- [Status](https://docs.zenml.io/api-reference/oss-api/oss-api/steps/status)
- [Logs](https://docs.zenml.io/api-reference/oss-api/oss-api/steps/logs)
- [Tags](https://docs.zenml.io/api-reference/oss-api/oss-api/tags)
- [Users](https://docs.zenml.io/api-reference/oss-api/oss-api/users)
- [Resource membership](https://docs.zenml.io/api-reference/oss-api/oss-api/users/resource-membership)
- [Current user](https://docs.zenml.io/api-reference/oss-api/oss-api/current-user)
- [Getting Started](https://docs.zenml.io/api-reference/pro-api/getting-started)
- [Pro API](https://docs.zenml.io/api-reference/pro-api/pro-api)
- [Tenants](https://docs.zenml.io/api-reference/pro-api/pro-api/tenants)
- [Deploy](https://docs.zenml.io/api-reference/pro-api/pro-api/tenants/deploy)
- [Deactivate](https://docs.zenml.io/api-reference/pro-api/pro-api/tenants/deactivate)
- [Members](https://docs.zenml.io/api-reference/pro-api/pro-api/tenants/members)
- [Tenant status](https://docs.zenml.io/api-reference/pro-api/pro-api/tenant-status)
- [Users](https://docs.zenml.io/api-reference/pro-api/pro-api/users)
- [Authorize server](https://docs.zenml.io/api-reference/pro-api/pro-api/users/authorize-server)
- [Me](https://docs.zenml.io/api-reference/pro-api/pro-api/users/me)
- [Invitations](https://docs.zenml.io/api-reference/pro-api/pro-api/invitations)
- [Releases](https://docs.zenml.io/api-reference/pro-api/pro-api/releases)
- [Devices](https://docs.zenml.io/api-reference/pro-api/pro-api/devices)
- [Verify](https://docs.zenml.io/api-reference/pro-api/pro-api/devices/verify)
- [Roles](https://docs.zenml.io/api-reference/pro-api/pro-api/roles)
- [Assignments](https://docs.zenml.io/api-reference/pro-api/pro-api/roles/assignments)
- [Permissions](https://docs.zenml.io/api-reference/pro-api/pro-api/permissions)
- [Teams](https://docs.zenml.io/api-reference/pro-api/pro-api/teams)
- [Members](https://docs.zenml.io/api-reference/pro-api/pro-api/teams/members)
- [Organizations](https://docs.zenml.io/api-reference/pro-api/pro-api/organizations)
- [Trial](https://docs.zenml.io/api-reference/pro-api/pro-api/organizations/trial)
- [Invitations](https://docs.zenml.io/api-reference/pro-api/pro-api/organizations/invitations)
- [Members](https://docs.zenml.io/api-reference/pro-api/pro-api/organizations/members)
- [Roles](https://docs.zenml.io/api-reference/pro-api/pro-api/organizations/roles)
- [Teams](https://docs.zenml.io/api-reference/pro-api/pro-api/organizations/teams)
- [Tenants](https://docs.zenml.io/api-reference/pro-api/pro-api/organizations/tenants)
- [Tenant](https://docs.zenml.io/api-reference/pro-api/pro-api/organizations/tenant)
- [Entitlement](https://docs.zenml.io/api-reference/pro-api/pro-api/organizations/entitlement)
- [Validation](https://docs.zenml.io/api-reference/pro-api/pro-api/organizations/validation)
- [Name](https://docs.zenml.io/api-reference/pro-api/pro-api/organizations/validation/name)
- [Tenant name](https://docs.zenml.io/api-reference/pro-api/pro-api/organizations/validation/tenant-name)
- [Health](https://docs.zenml.io/api-reference/pro-api/pro-api/health)
- [Usage event](https://docs.zenml.io/api-reference/pro-api/pro-api/usage-event)
- [Usage batch](https://docs.zenml.io/api-reference/pro-api/pro-api/usage-batch)
- [Stigg webhook](https://docs.zenml.io/api-reference/pro-api/pro-api/stigg-webhook)
- [Auth](https://docs.zenml.io/api-reference/pro-api/pro-api/auth)
- [Login](https://docs.zenml.io/api-reference/pro-api/pro-api/auth/login)
- [Connections](https://docs.zenml.io/api-reference/pro-api/pro-api/auth/connections)
- [Authorize](https://docs.zenml.io/api-reference/pro-api/pro-api/auth/authorize)
- [Callback](https://docs.zenml.io/api-reference/pro-api/pro-api/auth/callback)
- [Logout](https://docs.zenml.io/api-reference/pro-api/pro-api/auth/logout)
- [Device authorization](https://docs.zenml.io/api-reference/pro-api/pro-api/auth/device-authorization)
- [Api token](https://docs.zenml.io/api-reference/pro-api/pro-api/auth/api-token)
- [Tenant authorization](https://docs.zenml.io/api-reference/pro-api/pro-api/auth/tenant-authorization)
- [Rbac](https://docs.zenml.io/api-reference/pro-api/pro-api/rbac)
- [Check permissions](https://docs.zenml.io/api-reference/pro-api/pro-api/rbac/check-permissions)
- [Allowed resource ids](https://docs.zenml.io/api-reference/pro-api/pro-api/rbac/allowed-resource-ids)
- [Resource members](https://docs.zenml.io/api-reference/pro-api/pro-api/rbac/resource-members)
- [Server](https://docs.zenml.io/api-reference/pro-api/pro-api/server)
- [Info](https://docs.zenml.io/api-reference/pro-api/pro-api/server/info)

## SDK Reference

- [Overview](https://docs.zenml.io/sdk-reference/readme): See docstrings for ZenML Code
- [Client](https://docs.zenml.io/sdk-reference/client)