# Zenml

> Effective access management is crucial for maintaining security and efficiency in your ZenML projects. This guide will help you understand the different roles within a ZenML server and how to manage a

## Pages

- [Access Management](access-management.md): Effective access management is crucial for maintaining security and efficiency in your ZenML projects. This guide wil...
- [Advanced Features](advanced-features.md): This guide covers advanced features and capabilities of ZenML pipelines and steps, allowing you to build more sophist...
- [Airflow Orchestrator](airflow.md): ZenML pipelines can be executed natively as [Airflow](https://airflow.apache.org/) DAGs. This brings together the pow...
- [Alerters](alerters.md): **Alerters** allow you to send messages to chat services (like Slack, Discord, Mattermost, etc.) from within your pip...
- [Alibaba Cloud OSS](alibaba-oss.md): [Alibaba Cloud Object Storage Service (OSS)](https://www.alibabacloud.com/product/object-storage-service) is an S3-co...
- [Allowed resource ids](allowed-resource-ids.md): {% openapi src="" path="/rbac/allowed\_resource\_ids" method="get" %}
- [Annotators](annotators.md): Annotators are a stack component that enables the use of data annotation as part of your ZenML stack and pipelines. Y...
- [Api keys](api-keys.md): {% openapi src="" path="/api/v1/service\_accounts/{service\_...
- [Api token](api-token.md): {% openapi src="" path="/api/v1/api\_token" method="get" %}
- [Argilla](argilla.md): [Argilla](https://github.com/argilla-io/argilla) is a collaboration tool for AI engineers and domain experts who need...
- [Artifact Stores](artifact-stores.md): The Artifact Store is a central component in any MLOps stack. As the name suggests, it acts as a data persistence lay...
- [Artifact versions](artifact-versions.md): {% openapi src="" path="/api/v1/artifact\_versions" method="...
- [Artifact Log Store](artifact.md): The Artifact Log Store is the default log store flavor that comes built-in with ZenML. It stores logs directly in you...
- [Artifacts](artifacts.md): Artifacts are a cornerstone of ZenML's ML pipeline management system. This guide explains what artifacts are, how the...
- [Assignments](assignments.md): {% openapi src="" path="/roles/{role\_id}/assignments" method="get" %}
- [Introduction](auth-management.md): A production-grade MLOps platform involves interactions between a diverse combination of third-party libraries and ex...
- [Auth](auth.md): - [Login](/api-reference/pro-api/pro-api/auth/login.md)
- [Authorize server](authorize-server.md): {% openapi src="" path="/users/authorize\_server" method="get" %}
- [Authorize](authorize.md): {% openapi src="" path="/auth/authorize" method="get" %}
- [AWS App Runner Deployer](aws-app-runner.md): [AWS App Runner](https://aws.amazon.com/apprunner/) is a fully managed serverless platform that allows you to deploy ...
- [AWS](aws-guide.md): This page aims to quickly set up a minimal production stack on AWS. With just a few simple steps, you will set up an ...
- [AWS Service Connector](aws-service-connector.md): The ZenML AWS Service Connector facilitates the authentication and access to managed AWS services and resources. Thes...
- [Amazon Elastic Container Registry (ECR)](aws.md): The AWS container registry is a [container registry](https://docs.zenml.io/stacks/stack-components/container-registri...
- [Azure](azure-guide.md): This page aims to quickly set up a minimal production stack on Azure. With just a few simple steps, you will set up a...
- [Azure Service Connector](azure-service-connector.md): The ZenML Azure Service Connector facilitates the authentication and access to managed Azure services and resources. ...
- [Azure Blob Storage](azure.md): The Azure Artifact Store is an [Artifact Store](https://docs.zenml.io/stacks/stack-components/artifact-stores) flavor...
- [AzureML Orchestrator](azureml.md): [AzureML](https://azure.microsoft.com/en-us/products/machine-learning) is a cloud-based orchestration service provide...
- [Basic RAG inference pipeline](basic-rag-inference-pipeline.md): Now that we have our index store, we can use it to make queries based on the\
- [Batch](batch.md): {% openapi src="" path="/api/v1/artifact\_versions/batch" me...
- [BentoML](bentoml.md): BentoML is an open-source framework for machine learning model serving. it can be used to deploy models locally, in a...
- [Best practices for upgrading](best-practices-upgrading-zenml.md): Upgrading ZenML doesn't have to be scary.
- [Best practices](best-security-practices.md): Service Connector Types, especially those targeted at cloud providers, offer a plethora of authentication methods mat...
- [Cache previous executions](cache-previous-executions.md): Developing machine learning pipelines is iterative in nature. ZenML speeds up development in this work with step cach...
- [Callback](callback.md): {% openapi src="" path="/auth/callback" method="get" %}
- [Check permissions](check-permissions.md): {% openapi src="" path="/rbac/check\_permissions" method="get" %}
- [Choosing an Orchestrator](choose-orchestration-environment.md): When embarking on a machine learning project, one of the most critical early decisions is where to run your pipelines...
- [Set up CI/CD](ci-cd.md): Until now, we have been executing ZenML pipelines locally. While this is a good mode of operating pipelines, in produ...
- [Client](client.md): {% openapi src="" path="/api/v1/service\_connectors/{connect...
- [Orchestrate on the cloud](cloud-orchestration.md): Until now, we've only run pipelines locally. The next step is to get free from our local machines and transition our ...
- [Code Repositories](code-repositories.md): A code repository in ZenML refers to a remote storage location for your code. Some commonly known code repository pla...
- [Comet](comet.md): The Comet Experiment Tracker is an [Experiment Tracker](https://docs.zenml.io/stacks/stack-components/experiment-trac...
- [Community & content](community-and-content.md): The ZenML team and community have put together a list of references that can be used to get in touch with the develop...
- [Overview](component-guide.md): If you are new to the world of MLOps, it is often daunting to be immediately faced with a sea of tools that seemingly...
- [Component types](component-types.md): {% openapi src="" path="/api/v1/component-types" method="get...
- [Components](components.md): {% openapi src="" path="/api/v1/components" method="get" %}
- [Control Plane](config-control-plane.md): This page provides the configuration reference for the ZenML Control Plane. For an overview of what the Control Plane...
- [Workspace Server](config-workspace-server.md): This page provides the configuration reference for the ZenML Workspace Server, including the workload manager that en...
- [Configuration Details](configuration-details.md): This section provides reference documentation for configuring each ZenML Pro component. Use these guides to understan...
- [Configuration](configuration.md): ZenML provides several approaches to configure your pipelines and steps:
- [Configure your pipeline to add compute](configure-pipeline.md): Now that we have our pipeline up and running in the cloud, you might be wondering how ZenML figured out what sort of ...
- [Configure Python environments](configure-python-environments.md): ZenML deployments often involve multiple environments. This guide helps you manage dependencies and configurations ac...
- [Configure a code repository](connect-code-repository.md): Throughout the lifecycle of a MLOps pipeline, it can get quite tiresome to always wait for a Docker build every time ...
- [with your User (interactive)](connect-in-with-your-user-interactive.md): You can authenticate your clients with the ZenML Server using the ZenML CLI and the web‑based login (device flow). Th...
- [with your User (programmatic)](connect-with-a-pat.md): If you are using ZenML Pro and need to call the ZenML Pro workspace API from a non-interactive environment, you also ...
- [with a Service Account](connect-with-a-service-account.md): {% hint style="warning" %}
- [Connect](connecting-to-zenml.md): Once [ZenML is deployed](https://docs.zenml.io/deploying-zenml/deploying-zenml), there are various ways to connect to...
- [Connections](connections.md): {% openapi src="" path="/auth/connections" method="get" %}
- [Connector Types](connector-types.md): - [Docker Service Connector](/stacks/service-connectors/connector-types/docker-service-connector.md): Configuring Doc...
- [Container Registries](container-registries.md): The container registry is an essential part of most remote MLOps stacks. It is used to store container images that ar...
- [Containerization](containerization.md): ZenML executes pipeline steps sequentially in the active Python environment when running locally. However, with remot...
- [Core Concepts](core-concepts.md): **ZenML** is a unified, extensible, open-source MLOps framework for creating portable, production-ready **MLOps pipel...
- [Create an ML pipeline](create-an-ml-pipeline.md): In the quest for production-ready ML models, workflows can quickly become complex. Decoupling and standardizing stage...
- [Current user](current-user.md): {% openapi src="" path="/api/v1/current-user" method="get" %}
- [Custom secret stores](custom-secret-stores.md): The secrets store acts as the one-stop shop for all the secrets to which your pipeline or stack components might need...
- [Custom Stack Component](custom-stack-component.md): When building a sophisticated MLOps Platform, you will often need to come up with custom-tailored solutions for your ...
- [Develop a custom orchestrator](custom.md): {% hint style="info" %}
- [Dashboard](dashboard-features.md): The ZenML dashboard is a powerful web-based interface that provides visualization, management, and analysis capabilit...
- [Data ingestion and preprocessing](data-ingestion.md): The first step in setting up a RAG pipeline is to ingest the data that will be\
- [Data Validators](data-validators.md): Without good data, even the best machine learning models will yield questionable results. A lot of effort goes into e...
- [Databricks Orchestrator](databricks.md): [Databricks](https://www.databricks.com/) is a unified data analytics platform that combines the best of data warehou...
- [Datadog Log Store](datadog.md): The Datadog Log Store is a log store flavor that exports logs to [Datadog's log management platform](https://www.data...
- [Managing machine learning datasets](datasets.md): As machine learning projects grow in complexity, you often need to work with various data sources and manage intricat...
- [Deactivate](deactivate.md): {% openapi src="" path="/tenants/{tenant\_id}/deactivate" method="patch" %}
- [Debugging and Solving Issues](debug-and-solve-issues.md): If you stumbled upon this page, chances are you're facing issues with using ZenML. This page documents suggestions an...
- [Deepchecks](deepchecks.md): The Deepchecks [Data Validator](https://docs.zenml.io/stacks/stack-components/data-validators) flavor provided with t...
- [Default Container Registry](default.md): The Default container registry is a [container registry](https://docs.zenml.io/stacks/stack-components/container-regi...
- [Terraform Modules](deploy-a-cloud-stack-with-terraform.md): ZenML maintains a collection of [Terraform modules](https://registry.terraform.io/modules/zenml-io/zenml-stack) desig...
- [1-click Deployment](deploy-a-cloud-stack.md): In ZenML, the [stack](https://docs.zenml.io/user-guides/production-guide/understand-stacks) is a fundamental concept ...
- [Deploy using HuggingFace Spaces](deploy-using-huggingface-spaces.md): A quick way to deploy ZenML and get started is to use [HuggingFace Spaces](https://huggingface.co/spaces). HuggingFac...
- [Deploy with custom images](deploy-with-custom-image.md): In most cases, deploying ZenML with the default`zenmlhub/zenml-server`Docker image should work just fine. However, ...
- [Deploy with Docker](deploy-with-docker.md): The ZenML server container image is available at [`zenmldocker/zenml-server`](https://hub.docker.com/r/zenmldocker/ze...
- [Deploy with Helm](deploy-with-helm.md): If you wish to manually deploy and manage ZenML in a Kubernetes cluster of your choice, ZenML also includes a Helm ch...
- [Deploy](deploy.md): {% openapi src="" path="/tenants/{tenant\_id}/deploy" method="patch" %}
- [Deployers](deployers.md): Pipeline deployment is the process of making ZenML pipelines available as long-running HTTP services for real-time ex...
- [Deploying finetuned models](deploying-finetuned-models.md): Deploying your finetuned LLM is a critical step in bringing your custom finetuned model into a place where it can be ...
- [Deploy](deploying-zenml.md): Moving your ZenML Server to a production environment offers several benefits over staying local:
- [Pipeline Deployments](deployment.md): Pipeline deployment allows you to run ZenML pipelines as long-running HTTP services for real-time execution, rather t...
- [Deployment Settings](deployment-settings.md): ZenML pipeline deployments run an ASGI application under a production-grade`uvicorn`server. This makes your pipelin...
- [Device authorization](device-authorization.md): {% openapi src="" path="/api/v1/device\_authorization" metho...
- [Devices](devices.md): {% openapi src="" path="/devices" method="get" %}
- [Discord Alerter](discord.md): The`DiscordAlerter`enables you to send messages to a dedicated Discord channel directly from within your ZenML pipe...
- [Train with GPUs](distributed-training.md): Need more compute than your laptop can offer? This tutorial shows how to:
- [Docker Service Connector](docker-service-connector.md): The ZenML Docker Service Connector allows authenticating with a Docker or OCI container registry and managing Docker ...
- [Docker Deployer](docker.md): The Docker deployer is a [deployer](https://docs.zenml.io/stacks/stack-components/deployers) flavor that comes built-...
- [DockerHub](dockerhub.md): The DockerHub container registry is a [container registry](https://docs.zenml.io/stacks/stack-components/container-re...
- [Dynamic Pipelines (Experimental)](dynamic-pipelines.md): {% hint style="info" %}
- [Embeddings generation](embeddings-generation.md): In this section, we'll explore how to generate embeddings for your data to\
- [An end-to-end project](end-to-end.md): That was awesome! We learned so many advanced MLOps production concepts:
- [Entitlement](entitlement.md): {% openapi src="" path="/organizations/{organization\_id}/entitlement/{featur...
- [Environment Variables](environment-variables.md): Environment variables can be configured to be available at runtime during step execution. ZenML provides two ways to ...
- [Evaluating finetuned embeddings](evaluating-finetuned-embeddings.md): Now that we've finetuned our embeddings, we can evaluate them and compare to the base embeddings. We have all the dat...
- [Evaluating reranking performance](evaluating-reranking-performance.md): We've already set up an evaluation pipeline, so adding reranking evaluation is relatively straightforward. In this se...
- [Evaluation for finetuning](evaluation-for-finetuning.md): Evaluations (evals) for Large Language Model (LLM) finetuning are akin to unit tests in traditional software developm...
- [Evaluation in 65 lines of code](evaluation-in-65-loc.md): Our RAG guide included [a short example](https://docs.zenml.io/user-guides/llmops-guide/rag-with-zenml/rag-85-loc) fo...
- [Evaluation in practice](evaluation-in-practice.md): Now that we've seen individually how to evaluate the retrieval and generation components of our pipeline, it's worth ...
- [Evaluation and metrics](evaluation.md): In this section, we'll explore how to evaluate the performance of your RAG pipeline using metrics and visualizations....
- [Evidently](evidently.md): The Evidently [Data Validator](https://docs.zenml.io/stacks/stack-components/data-validators) flavor provided with th...
- [Example usages](example-usages.md): Pipelines, runs, stacks, and many other ZenML resources are stored and versioned in a database within your ZenML inst...
- [Execution](execution.md): This page explains what happens under the hood when ZenML executes steps in static and dynamic pipelines. Regardless ...
- [Experiment Trackers](experiment-trackers.md): Experiment trackers let you track your ML experiments by logging extended information about your models, datasets, me...
- [FAQ](faq.md): This page addresses common questions about ZenML, including general information about the project and how to accompli...
- [Feast](feast.md): Feast (Feature Store) is an operational data system for managing and serving machine learning features to models in p...
- [Feature Stores](feature-stores.md): Feature stores allow data teams to serve data via an offline store and an online low-latency store where data is kept...
- [Inspecting past pipeline runs](fetching-pipelines.md): Ever trained a model yesterday and forgotten where its artifacts are stored? This tutorial shows you how to:
- [Finetuning in 100 lines of code](finetuning-100-loc.md): There's a lot to understand about LLM fine-tuning - from choosing the right base model to preparing your dataset and ...
- [Finetuning embeddings with Sentence Transformers](finetuning-embeddings-with-sentence-transformers.md): We now have a dataset that we can use to finetune our embeddings. You can[inspect the positive and negative examples]...
- [Improve retrieval by finetuning embeddings](finetuning-embeddings.md): We previously learned [how to use RAG with ZenML](https://docs.zenml.io/user-guides/llmops-guide/rag-with-zenml) to b...
- [Finetuning LLMs with ZenML](finetuning-llms.md): So far in our LLMOps journey we've learned [how to use RAG with ZenML](https://docs.zenml.io/user-guides/llmops-guide...
- [Finetuning with 🤗 Accelerate](finetuning-with-accelerate.md): We're finally ready to get our hands on the code and see how it works. In this\
- [Full stack resources](full-stack-resources.md): {% openapi src="" path="/api/v1/service\_connectors/full\_st...
- [GCP Cloud Run Deployer](gcp-cloud-run.md): [GCP Cloud Run](https://cloud.google.com/run) is a fully managed serverless platform that allows you to deploy and ru...
- [GCP](gcp-guide.md): This page aims to quickly set up a minimal production stack on GCP. With just a few simple steps you will set up a se...
- [GCP Service Connector](gcp-service-connector.md): The ZenML GCP Service Connector facilitates the authentication and access to managed GCP services and resources. Thes...
- [Google Cloud Storage (GCS)](gcp.md): The GCS Artifact Store is an [Artifact Store](https://docs.zenml.io/stacks/stack-components/artifact-stores) flavor p...
- [Generation evaluation](generation.md): Now that we have a sense of how to evaluate the retrieval component of our RAG\
- [Getting Started](getting-started.md): The ZenML OSS server is a FastAPI application, therefore the OpenAPI-compliant docs are available at`/docs`or`/red...
- [GitHub Container Registry](github.md): The GitHub container registry is a [container registry](https://docs.zenml.io/stacks/stack-components/container-regis...
- [Global settings](global-settings.md): The information about the global settings of ZenML on a machine is kept in a folder commonly referred to as the **Zen...
- [Great Expectations](great-expectations.md): The Great Expectations [Data Validator](https://docs.zenml.io/stacks/stack-components/data-validators) flavor provide...
- [Health](health.md): {% openapi src="" path="/health" method="get" %}
- [Hello World](hello-world.md): This guide will help you build and deploy your first ZenML pipeline, starting locally and then transitioning to the c...
- [Hierarchy](hierarchy.md): In ZenML Pro, there is a slightly different entity hierarchy as compared to the open-source ZenML\
- [Hugging Face Deployer](huggingface.md): [Hugging Face Spaces](https://huggingface.co/spaces) is a platform for hosting and sharing machine learning applicati...
- [AWS ECS](hybrid-deployment-ecs.md): This guide provides high-level instructions for deploying ZenML Pro in a Hybrid setup on AWS ECS (Elastic Container S...
- [Kubernetes with Helm](hybrid-deployment-helm.md): This guide provides step-by-step instructions for deploying ZenML Pro in a Hybrid setup using Kubernetes and Helm cha...
- [Hybrid](hybrid-deployment.md): ZenML Pro Hybrid SaaS offers the perfect balance between control and convenience. While ZenML manages user authentica...
- [Hyper-parameter tuning](hyper-parameter-tuning.md): Hyper‑parameter tuning is the process of systematically searching for the best set of hyper‑parameters for your model...
- [HyperAI Service Connector](hyperai-service-connector.md): The ZenML HyperAI Service Connector allows authenticating with a HyperAI instance for deployment of pipeline runs. Th...
- [HyperAI Orchestrator](hyperai.md): [HyperAI](https://www.hyperai.ai) is a cutting-edge cloud compute platform designed to make AI accessible for everyon...
- [Infrastructure as Code with Terraform](iac.md): You're a system architect tasked with setting up a scalable ML infrastructure that needs to:
- [Image Builders](image-builders.md): The image builder is an essential part of most remote MLOps stacks. It is used to build container images such that yo...
- [Custom Integration](implement-a-custom-integration.md): One of the main goals of ZenML is to find some semblance of order in the ever-growing MLOps landscape. ZenML already ...
- [Implementing reranking in ZenML](implementing-reranking.md): We already have a working RAG pipeline, so inserting a reranker into the\
- [Info](info.md): {% openapi src="" path="/api/v1/info" method="get" %}
- [Infrastructure as code](infrastructure-as-code.md): [Infrastructure as Code (IaC)](https://aws.amazon.com/what-is/iac) is\
- [Installation](installation.md): {% stepper %}
- [Integrations](integrations.md): Categorizing the MLOps stack is a good way to write abstractions for an MLOps pipeline and standardize your processes...
- [Welcome to ZenML](introduction.md): ZenML is a unified MLOps framework that extends the battle-tested principles you rely on for classical ML to the new ...
- [Invitations](invitations.md): {% openapi src="" path="/invitations/{invitation\_id}" method="get" %}
- [Kaniko Image Builder](kaniko.md): {% hint style="warning" %}
- [Keep Your Dashboard Clean](keep-your-dashboard-server-clean.md): When developing pipelines, it's common to run and debug them multiple times. To avoid cluttering the server with thes...
- [Kubeflow Orchestrator](kubeflow.md): The Kubeflow orchestrator is an [orchestrator](https://docs.zenml.io/stacks/stack-components/orchestrators) flavor pr...
- [Kubernetes Service Connector](kubernetes-service-connector.md): The ZenML Kubernetes service connector facilitates authenticating and connecting to a Kubernetes cluster. The connect...
- [Kubernetes Orchestrator](kubernetes.md): Using the ZenML`kubernetes`integration, you can orchestrate and scale your ML pipelines on a [Kubernetes](https://k...
- [Label Studio](label-studio.md): Label Studio is one of the leading open-source annotation platforms available to data scientists and ML practitioners...
- [Legacy docs](legacy-docs.md)
- [Lightning AI Orchestrator](lightning.md): [Lightning AI Studio](https://lightning.ai/) is a platform that simplifies the development and deployment of AI appli...
- [LLMOps guide](llmops-guide.md): Welcome to the ZenML LLMOps Guide, where we dive into the exciting world of Large Language Models (LLMs) and how to i...
- [LLM Tooling](llms-txt.md): ZenML provides multiple ways to enhance your AI-assisted development workflow:
- [Local Docker Orchestrator](local-docker.md): The local Docker orchestrator is an [orchestrator](https://docs.zenml.io/stacks/stack-components/orchestrators) flavo...
- [Local Orchestrator](local.md): The local orchestrator is an [orchestrator](https://docs.zenml.io/stacks/stack-components/orchestrators) flavor that ...
- [Log Stores](log-stores.md): The log store is a stack component responsible for collecting, storing, and retrieving logs generated during pipeline...
- [Logging](logging.md): By default, ZenML uses a logging handler to capture two types of logs:
- [Login](login.md): {% openapi src="" path="/api/v1/login" method="post" %}
- [Logout](logout.md): {% openapi src="" path="/api/v1/logout" method="get" %}
- [Logs](logs.md): {% openapi src="" path="/api/v1/logs/{logs\_id}" method="get...
- [Manage artifacts](manage-artifacts.md): Data sits at the heart of every machine learning workflow. Managing and versioning this data correctly is essential f...
- [Handling big data](manage-big-data.md): As your datasets grow, a single‑machine pandas workflow eventually hits its limits. This tutorial walks you through *...
- [Managing scheduled pipelines](managing-scheduled-pipelines.md): This tutorial demonstrates how to work with scheduled pipelines in ZenML through a practical example. We'll create a ...
- [Materializers](materializers.md): Materializers are a core concept in ZenML that enable the serialization, storage, and retrieval of artifacts in your ...
- [Leveraging MCP](mcp-chat-with-server.md): ZenML server supports a chat interface that allows you to interact with the server using natural language through the...
- [Me](me.md): {% openapi src="" path="/users/me" method="get" %}
- [Members](members.md): {% openapi src="" path="/tenants/{tenant\_id}/members" method="get" %}
- [Metadata](metadata.md): Metadata in ZenML provides critical context to your ML workflows, allowing you to track additional information about ...
- [Migration guide](migration-guide.md): Migrations are necessary for ZenML releases that include breaking changes, which are currently all releases that incr...
- [Migration guide 0.39.1 → 0.41.0](migration-zero-forty.md): ZenML versions 0.40.0 to 0.41.0 introduced a new and more flexible syntax to define ZenML steps and pipelines. This p...
- [Migration guide 0.58.2 → 0.60.0](migration-zero-sixty.md): ZenML now uses Pydantic v2. 🥳
- [Migration guide 0.23.0 → 0.30.0](migration-zero-thirty.md): {% hint style="warning" %}
- [Migration guide 0.13.2 → 0.20.0](migration-zero-twenty.md): *Last updated: 2023-07-24*
- [MinIO](minio.md): [MinIO](https://min.io/) is a high-performance, S3-compatible object storage system. Since MinIO provides a fully S3-...
- [MLflow](mlflow.md): The MLflow Experiment Tracker is an [Experiment Tracker](https://docs.zenml.io/stacks/stack-components/experiment-tra...
- [Modal](modal.md): [Modal](https://modal.com) is a platform for running cloud infrastructure. It offers specialized compute instances to...
- [Model Deployers](model-deployers.md): {% hint style="warning" %}
- [Model Registries](model-registries.md): Model registries are centralized storage solutions for managing and tracking machine learning models across various s...
- [Model versions](model-versions.md): {% openapi src="" path="/api/v1/models/{model\_name\_or\_id}...
- [Models](models.md): Machine learning models and AI agent configurations are at the heart of any ML workflow and AI system. ZenML provides...
- [Name](name.md): {% openapi src="" path="/organizations/validation/name/{organization\_name}" ...
- [Neptune](neptune.md): {% hint style="warning" %}
- [Next steps](next-steps.md): At this point, hopefully you've gone through the suggested stages of iteration to improve and learn more about how to...
- [Orchestrators](orchestrators.md): The orchestrator is an essential component in any MLOps stack as it is responsible for running your machine learning ...
- [Organizations](organization.md): ZenML Pro arranges various aspects of your work experience around the concept of an **Organization**. This is the top...
- [Organizations](organizations.md): {% openapi src="" path="/organizations" method="get" %}
- [Organizing Stacks Pipelines Models](organizing-pipelines-and-models.md): This cookbook demonstrates how to effectively organize your machine learning assets in ZenML using tags and projects....
- [OSS API](oss-api.md): - [Artifacts](/api-reference/oss-api/oss-api/artifacts.md)
- [OpenTelemetry Log Store](otel.md): The OpenTelemetry (OTEL) Log Store is a log store flavor that exports logs to any OpenTelemetry-compatible backend us...
- [Permissions](permissions.md): {% openapi src="" path="/permissions" method="get" %}
- [Personal Access Tokens](personal-access-tokens.md): Personal Access Tokens (PATs) in ZenML Pro provide a secure way to authenticate your user account programmatically wi...
- [Pigeon](pigeon.md): Pigeon is a lightweight, open-source annotation tool designed for quick and easy labeling of data directly within Jup...
- [Pipeline configuration](pipeline-configuration.md): {% openapi src="" path="/api/v1/runs/{run\_id}/pipeline-conf...
- [Pipelines](pipelines.md): {% openapi src="" path="/api/v1/pipelines" method="get" %}
- [Pro API](pro-api.md): - [Tenants](/api-reference/pro-api/pro-api/tenants.md)
- [Pro Control Plane](pro-control-plane.md): Stay up to date with the latest features, improvements, and fixes in ZenML Pro.
- [Prodigy](prodigy.md): [Prodigy](https://prodi.gy/) is a modern annotation tool for creating training and evaluation data for machine learni...
- [Production guide](production-guide.md): The ZenML production guide builds upon the [Starter guide](https://docs.zenml.io/user-guides/starter-guide) and is th...
- [Creating Templates for ML Platform](project-templates.md): What would you need to get a quick understanding of the ZenML framework and start building your ML pipelines? The ans...
- [Projects](projects.md): Projects in ZenML Pro provide a logical subdivision within workspaces, allowing you to organize and manage your MLOps...
- [5-minute Quick Wins](quick-wins.md): Below is a menu of 5-minute quick wins you can sprinkle into an existing ZenML project with almost no code changes. E...
- [RAG in 85 lines of code](rag-85-loc.md): There's a lot of theory and context to think about when it comes to RAG, but\
- [RAG with ZenML](rag-with-zenml.md): Retrieval-Augmented Generation (RAG) is a powerful technique that combines the\
- [Rbac](rbac.md): - [Check permissions](/api-reference/pro-api/pro-api/rbac/check-permissions.md)
- [Overview](readme.md): Discover how to build production-ready ML pipelines with ZenML through our curated learning resources. Whether you're...
- [Refresh](refresh.md): {% openapi src="" path="/api/v1/runs/{run\_id}/refresh" meth...
- [Register a cloud stack](register-a-cloud-stack.md): In ZenML, the [stack](https://docs.zenml.io/user-guides/production-guide/understand-stacks) is a fundamental concept ...
- [Releases](releases.md): {% openapi src="" path="/releases" method="get" %}
- [Connecting remote storage](remote-storage.md): In the previous chapters, we've been working with artifacts stored locally on our machines. This setup is fine for in...
- [Reranking for better retrieval](reranking.md): Rerankers are a crucial component of retrieval systems that use LLMs. They help\
- [Resource members](resource-members.md): {% openapi src="" path="/rbac/resource\_members" method="get" %}
- [Resource membership](resource-membership.md): {% openapi src="" path="/api/v1/users/{user\_name\_or\_id}/r...
- [Retrieval evaluation](retrieval.md): The retrieval component of our RAG pipeline is responsible for finding relevant\
- [Roles & Permissions](roles.md): ZenML Pro offers a robust role-based access control (RBAC) system to manage permissions across your organization, wor...
- [Rotate](rotate.md): {% openapi src="" path="/api/v1/service\_accounts/{service\_...
- [Running notebooks remotely](run-remote-notebooks.md): A Jupyter notebook is often the fastest way to prototype an ML experiment, but sooner or later you will want to execu...
- [Run templates](run-templates.md): {% openapi src="" path="/api/v1/run\_templates" method="get" %}
- [Runs](runs.md): {% openapi src="" path="/api/v1/model\_versions/{model\_vers...
- [Amazon Simple Cloud Storage (S3)](s3.md): The S3 Artifact Store is an [Artifact Store](https://docs.zenml.io/stacks/stack-components/artifact-stores) flavor pr...
- [SaaS](saas-deployment.md): ZenML Pro SaaS is the fastest and easiest way to get started with enterprise-grade MLOps. With zero infrastructure se...
- [AWS Sagemaker Orchestrator](sagemaker.md): [Sagemaker Pipelines](https://aws.amazon.com/sagemaker/pipelines) is a serverless ML workflow tool running on AWS. It...
- [Scenarios](scenarios.md): ZenML Pro offers three flexible deployment options to match your organization's security, compliance, and operational...
- [Schedules](schedules.md): {% openapi src="" path="/api/v1/schedules" method="get" %}
- [Scheduling](scheduling.md): {% hint style="info" %}
- [Secret management](secret-management.md): ZenML provides a centralized secrets management system that allows you to register and manage secrets in a secure way...
- [Secrets Stores](secrets-stores.md): The secrets you configure in your ZenML Pro workspaces are by default stored in the same database as your other works...
- [Secrets](secrets.md): ZenML secrets are groupings of **key-value pairs** which are securely stored in the ZenML secrets store. Additionally...
- [Seldon](seldon.md): [Seldon Core](https://github.com/SeldonIO/seldon-core) is a production grade source-available model serving platform....
- [Kubernetes with Helm](self-hosted-deployment-helm.md): This guide provides step-by-step instructions for deploying ZenML Pro in a fully air-gapped setup on Kubernetes using...
- [Self-hosted](self-hosted-deployment.md): ZenML Pro Self-hosted deployment provides complete control and data sovereignty for organizations with the strictest ...
- [Self-hosted deployment](self-hosted.md): This page provides instructions for installing ZenML Pro - the ZenML Pro Control Plane and one or more ZenML Pro Work...
- [Server & SDK](server-sdk.md): Stay up to date with the latest features, improvements, and fixes in ZenML OSS.
- [Server](server.md): - [Info](/api-reference/pro-api/pro-api/server/info.md)
- [Service Accounts](service-accounts.md): Service accounts in ZenML Pro provide a secure way to authenticate automated systems, CI/CD pipelines, and other non-...
- [Complete guide](service-connectors-guide.md): This documentation section contains everything that you need to use Service Connectors to connect ZenML to external r...
- [Service connectors](service-connectors.md): {% openapi src="" path="/api/v1/service\_connectors" method=...
- [Service Connectors](service-connectors-2.md): Service Connectors provide a unified way to handle authentication between ZenML and external services like cloud prov...
- [Services](services.md): {% openapi src="" path="/api/v1/services" method="get" %}
- [Setting up a Project Repository](set-up-your-repository.md): Welcome to the guide on setting up a well-architected ZenML project. This section will provide you with a comprehensi...
- [Shared Components for Teams](shared-components-for-teams.md): Teams often need to collaborate on projects, share versioned logic, and implement cross-cutting functionality that be...
- [Skypilot VM Orchestrator](skypilot-vm.md): The SkyPilot VM Orchestrator is an integration provided by ZenML that allows you to provision and manage virtual mach...
- [Slack Alerter](slack.md): The`SlackAlerter`enables you to send messages or ask questions within a dedicated Slack channel directly from withi...
- [Pipeline Snapshots](snapshots.md): A **Pipeline Snapshot** is an immutable snapshot of your pipeline that includes the pipeline DAG, code, configuration...
- [Source Code and Imports](sources.md): When ZenML interacts with your pipeline code, it needs to understand how to locate and import your code. This page ex...
- [Spark](spark-kubernetes.md): The`spark`integration brings two different step operators:
- [Stack & Components](stack-components.md): A [ZenML stack](https://docs.zenml.io/stacks) is a collection of components that together form an MLOps infrastructur...
- [Stacks](stacks.md): {% openapi src="" path="/api/v1/stacks" method="get" %}
- [Starter choices with finetuning](starter-choices-for-finetuning-llms.md): Finetuning large language models can be a powerful way to tailor their\
- [Starter guide](starter-guide.md): Welcome to the ZenML Starter Guide! If you're an MLOps engineer aiming to build robust ML platforms, or a data scient...
- [A starter project](starter-project.md): By now, you have understood some of the basic pillars of a MLOps system:
- [Status](status.md): {% openapi src="" path="/api/v1/runs/{run\_id}/status" metho...
- [Step configuration](step-configuration.md): {% openapi src="" path="/api/v1/steps/{step\_id}/step-config...
- [Step Operators](step-operators.md): The step operator enables the execution of individual pipeline steps in specialized runtime environments that are opt...
- [Steps](steps.md): {% openapi src="" path="/api/v1/runs/{run\_id}/steps" method...
- [Steps & Pipelines](steps-and-pipelines.md): Steps and Pipelines are the fundamental building blocks of ZenML. A **Step** is a reusable unit of computation, and a...
- [Stigg webhook](stigg-webhook.md): {% openapi src="" path="/stigg-webhook" method="post" %}
- [Storing embeddings in a vector database](storing-embeddings-in-a-vector-database.md): The process of generating the embeddings doesn't take too long, especially if the machine on which the step is runnin...
- [Synthetic data generation](synthetic-data-generation.md): We already have [a dataset of technical documentation](https://huggingface.co/datasets/zenml/rag_qa_embedding_questio...
- [System Architecture](system-architecture.md): ZenML Pro's architecture consists of two core services that work together to execute, track, and manage your ML pipel...
- [System Architecture](system-architectures.md): This guide walks through the various ways that ZenML can be deployed, from self-hosted OSS to\
- [Tags](tags.md): Organizing and categorizing your machine learning artifacts and models can\
- [Teams](teams.md): ZenML Pro introduces the concept of Teams to help you manage groups of users efficiently. A team is a collection of u...
- [Tekton Orchestrator](tekton.md): [Tekton](https://tekton.dev/) is a powerful and flexible open-source framework for creating CI/CD systems, allowing d...
- [Templates](templates.md): {% hint style="warning" %}
- [Tenant authorization](tenant-authorization.md): {% openapi src="" path="/auth/tenant\_authorization/{tenant\_id}" method="pos...
- [Tenant name](tenant-name.md): {% openapi src="" path="/organizations/{organization\_id}/validation/tenant\_...
- [Tenant status](tenant-status.md): {% openapi src="" path="/tenant\_status" method="patch" %}
- [Tenant](tenant.md): {% openapi src="" path="/organizations/{organization\_id}/tenant/{tenant\_nam...
- [Tenants](tenants.md): {% openapi src="" path="/tenants" method="get" %}
- [Track ML models](track-ml-models.md): As discussed in the [Core Concepts](https://docs.zenml.io/getting-started/core-concepts), ZenML also contains the not...
- [Trial](trial.md): {% openapi src="" path="/organizations/{organization\_id}/trial" method="get" %}
- [Trigger pipelines from external systems](trigger-pipelines-from-external-systems.md): This tutorial demonstrates practical approaches to triggering ZenML pipelines from external systems. We'll explore mu...
- [Troubleshoot your ZenML server](troubleshoot-your-deployed-server.md): In this document, we will go over some common issues that you might face when deploying ZenML and how to solve them.
- [Understanding stacks](understand-stacks.md): Now that we have ZenML deployed, we can take the next steps in making sure that our machine learning workflows are pr...
- [Understanding Retrieval-Augmented Generation (RAG)](understanding-rag.md): LLMs are powerful but not without their limitations. They are prone to generating incorrect responses, especially whe...
- [Understanding reranking](understanding-reranking.md): Reranking is the process of refining the initial ranking of documents retrieved\
- [Manage](upgrade-zenml-server.md): The way to upgrade your ZenML server depends a lot on how you deployed it. However, there are some best practices tha...
- [Control Plane](upgrades-control-plane.md): This page covers upgrade procedures for the ZenML Control Plane across different deployment scenarios.
- [Upgrades and Updates](upgrades-updates.md): This section covers upgrading ZenML Pro components for all deployment types. Each component has its own upgrade proce...
- [Workspace Server](upgrades-workspace-server.md): This page covers upgrade procedures for ZenML Workspace Servers across different deployment scenarios.
- [Usage batch](usage-batch.md): {% openapi src="" path="/usage-batch" method="post" %}
- [Usage event](usage-event.md): {% openapi src="" path="/usage-event" method="post" %}
- [Users](users.md): {% openapi src="" path="/api/v1/users" method="get" %}
- [Using ZenML server in production](using-zenml-server-in-prod.md): Setting up a ZenML server for testing is a quick process. However, most people have to move beyond so-called 'day zer...
- [Validation](validation.md): - [Name](/api-reference/pro-api/pro-api/organizations/validation/name.md)
- [Verify](verify.md): {% openapi src="" path="/api/v1/service\_connectors/verify" ...
- [Google Cloud VertexAI Orchestrator](vertex.md): [Vertex AI Pipelines](https://cloud.google.com/vertex-ai/docs/pipelines/introduction) is a serverless ML workflow too...
- [Google Cloud VertexAI Experiment Tracker](vertexai.md): The Vertex AI Experiment Tracker is an [Experiment Tracker](https://docs.zenml.io/stacks/stack-components/experiment-...
- [Visualizations](visualizations.md): Data visualization is a powerful tool for understanding your ML pipeline outputs. ZenML provides built-in capabilitie...
- [Visualize](visualize.md): {% openapi src="" path="/api/v1/artifact\_versions/{artifact...
- [vLLM](vllm.md): [vLLM](https://docs.vllm.ai/en/latest/) is a fast and easy-to-use library for LLM inference and serving.
- [Using VS Code extension](vscode-extension.md): The ZenML VSCode extension is a tool that allows you to manage your ZenML server\
- [Weights & Biases](wandb.md): The Weights & Biases Experiment Tracker is an [Experiment Tracker](https://docs.zenml.io/stacks/stack-components/expe...
- [Why and when to finetune LLMs](why-and-when-to-finetune-llms.md): This guide is intended to be a practical overview that gets you started with\
- [Whylogs](whylogs.md): The whylogs/WhyLabs [Data Validator](https://docs.zenml.io/stacks/stack-components/data-validators) flavor provided w...
- [Workspaces](workspaces.md): {% hint style="info" %}
- [YAML Configuration](yaml-configuration.md): ZenML provides configuration capabilities through YAML files that allow you to customize pipeline and step behavior w...
- [Your First AI Pipeline](your-first-ai-pipeline.md): ZenML pipelines work the same for **classical ML**, **AI agents**, and **hybrid approaches**. Choose your path below ...