# Stack Components - [Orchestrators](https://docs.zenml.io/stacks/stack-components/orchestrators.md): Orchestrating the execution of ML pipelines. - [Local Orchestrator](https://docs.zenml.io/stacks/stack-components/orchestrators/local.md): Orchestrating your pipelines to run locally. - [Local Docker Orchestrator](https://docs.zenml.io/stacks/stack-components/orchestrators/local-docker.md): Orchestrating your pipelines to run in Docker. - [Kubeflow Orchestrator](https://docs.zenml.io/stacks/stack-components/orchestrators/kubeflow.md): Orchestrating your pipelines to run on Kubeflow. - [Kubernetes Orchestrator](https://docs.zenml.io/stacks/stack-components/orchestrators/kubernetes.md): Orchestrating your pipelines to run on Kubernetes clusters. - [Google Cloud VertexAI Orchestrator](https://docs.zenml.io/stacks/stack-components/orchestrators/vertex.md): Orchestrating your pipelines to run on Vertex AI. - [AWS Sagemaker Orchestrator](https://docs.zenml.io/stacks/stack-components/orchestrators/sagemaker.md): Orchestrating your pipelines to run on Amazon Sagemaker. - [AzureML Orchestrator](https://docs.zenml.io/stacks/stack-components/orchestrators/azureml.md): Orchestrating your pipelines to run on AzureML. - [Databricks Orchestrator](https://docs.zenml.io/stacks/stack-components/orchestrators/databricks.md): Orchestrating your pipelines to run on Databricks. - [Tekton Orchestrator](https://docs.zenml.io/stacks/stack-components/orchestrators/tekton.md): Orchestrating your pipelines to run on Tekton. - [Airflow Orchestrator](https://docs.zenml.io/stacks/stack-components/orchestrators/airflow.md): Orchestrating your pipelines to run on Airflow. - [Skypilot VM Orchestrator](https://docs.zenml.io/stacks/stack-components/orchestrators/skypilot-vm.md): Orchestrating your pipelines to run on VMs using SkyPilot. - [HyperAI Orchestrator](https://docs.zenml.io/stacks/stack-components/orchestrators/hyperai.md): Orchestrating your pipelines to run on HyperAI.ai instances. - [Lightning AI Orchestrator](https://docs.zenml.io/stacks/stack-components/orchestrators/lightning.md): Orchestrating your pipelines to run on Lightning AI. - [Develop a custom orchestrator](https://docs.zenml.io/stacks/stack-components/orchestrators/custom.md): Learning how to develop a custom orchestrator. - [Deployers](https://docs.zenml.io/stacks/stack-components/deployers.md): Deploy pipelines as HTTP services for real-time execution - [Local Deployer](https://docs.zenml.io/stacks/stack-components/deployers/local.md): Deploying pipelines on your local machine as background processes. - [Docker Deployer](https://docs.zenml.io/stacks/stack-components/deployers/docker.md): Deploying your pipelines locally with Docker. - [Kubernetes Deployer](https://docs.zenml.io/stacks/stack-components/deployers/kubernetes.md): Deploying your pipelines to Kubernetes clusters. - [AWS App Runner Deployer](https://docs.zenml.io/stacks/stack-components/deployers/aws-app-runner.md): Deploying your pipelines to AWS App Runner. - [GCP Cloud Run Deployer](https://docs.zenml.io/stacks/stack-components/deployers/gcp-cloud-run.md): Deploying your pipelines to GCP Cloud Run. - [Hugging Face Deployer](https://docs.zenml.io/stacks/stack-components/deployers/huggingface.md): Deploying your pipelines to Hugging Face Spaces. - [Artifact Stores](https://docs.zenml.io/stacks/stack-components/artifact-stores.md): Setting up a persistent storage for your artifacts. - [Local Artifact Store](https://docs.zenml.io/stacks/stack-components/artifact-stores/local.md): Storing artifacts on your local filesystem. - [Amazon Simple Cloud Storage (S3)](https://docs.zenml.io/stacks/stack-components/artifact-stores/s3.md): Storing artifacts in an AWS S3 bucket. - [Google Cloud Storage (GCS)](https://docs.zenml.io/stacks/stack-components/artifact-stores/gcp.md): Storing artifacts using GCP Cloud Storage. - [Azure Blob Storage](https://docs.zenml.io/stacks/stack-components/artifact-stores/azure.md): Storing artifacts using Azure Blob Storage - [Alibaba Cloud OSS](https://docs.zenml.io/stacks/stack-components/artifact-stores/alibaba-oss.md): Storing artifacts in Alibaba Cloud Object Storage Service (OSS). - [MinIO](https://docs.zenml.io/stacks/stack-components/artifact-stores/minio.md): Storing artifacts in MinIO object storage. - [Develop a custom artifact store](https://docs.zenml.io/stacks/stack-components/artifact-stores/custom.md): Learning how to develop a custom artifact store. - [Container Registries](https://docs.zenml.io/stacks/stack-components/container-registries.md): Setting up a storage for Docker images. - [Default Container Registry](https://docs.zenml.io/stacks/stack-components/container-registries/default.md): Storing container images locally. - [DockerHub](https://docs.zenml.io/stacks/stack-components/container-registries/dockerhub.md): Storing container images in DockerHub. - [Amazon Elastic Container Registry (ECR)](https://docs.zenml.io/stacks/stack-components/container-registries/aws.md): Storing container images in Amazon ECR. - [Google Cloud Container Registry](https://docs.zenml.io/stacks/stack-components/container-registries/gcp.md): Storing container images in GCP. - [Azure Container Registry](https://docs.zenml.io/stacks/stack-components/container-registries/azure.md): Storing container images in Azure. - [GitHub Container Registry](https://docs.zenml.io/stacks/stack-components/container-registries/github.md): Storing container images in GitHub. - [Develop a custom container registry](https://docs.zenml.io/stacks/stack-components/container-registries/custom.md): Learning how to develop a custom container registry. - [Log Stores](https://docs.zenml.io/stacks/stack-components/log-stores.md): Storing and retrieving logs from your ML pipelines. - [Artifact Log Store](https://docs.zenml.io/stacks/stack-components/log-stores/artifact.md): Storing logs in your artifact store. - [OpenTelemetry Log Store](https://docs.zenml.io/stacks/stack-components/log-stores/otel.md): Exporting logs to any OpenTelemetry-compatible backend. - [Datadog Log Store](https://docs.zenml.io/stacks/stack-components/log-stores/datadog.md): Exporting logs to Datadog's log management platform. - [Develop a Custom Log Store](https://docs.zenml.io/stacks/stack-components/log-stores/custom.md): Learning how to develop a custom log store. - [Step Operators](https://docs.zenml.io/stacks/stack-components/step-operators.md): Executing individual steps in specialized environments. - [Amazon SageMaker](https://docs.zenml.io/stacks/stack-components/step-operators/sagemaker.md): Executing individual steps in SageMaker. - [AzureML](https://docs.zenml.io/stacks/stack-components/step-operators/azureml.md): Executing individual steps in AzureML. - [Databricks](https://docs.zenml.io/stacks/stack-components/step-operators/databricks.md): Executing individual steps on Databricks. - [Google Cloud VertexAI](https://docs.zenml.io/stacks/stack-components/step-operators/vertex.md): Executing individual steps in Vertex AI. - [Kubernetes](https://docs.zenml.io/stacks/stack-components/step-operators/kubernetes.md): Executing individual steps in Kubernetes Pods. - [Run:AI](https://docs.zenml.io/stacks/stack-components/step-operators/runai.md): Executing individual steps on Run:AI clusters with fractional GPU support. - [Modal](https://docs.zenml.io/stacks/stack-components/step-operators/modal.md): Executing individual steps in Modal. - [Spark](https://docs.zenml.io/stacks/stack-components/step-operators/spark-kubernetes.md): Executing individual steps on Spark - [Develop a Custom Step Operator](https://docs.zenml.io/stacks/stack-components/step-operators/custom.md): Learning how to develop a custom step operator. - [Experiment Trackers](https://docs.zenml.io/stacks/stack-components/experiment-trackers.md): Logging and visualizing ML experiments. - [Comet](https://docs.zenml.io/stacks/stack-components/experiment-trackers/comet.md): Logging and visualizing experiments with Comet. - [MLflow](https://docs.zenml.io/stacks/stack-components/experiment-trackers/mlflow.md): Logging and visualizing experiments with MLflow. - [Neptune](https://docs.zenml.io/stacks/stack-components/experiment-trackers/neptune.md): Logging and visualizing experiments with neptune.ai - [Weights & Biases](https://docs.zenml.io/stacks/stack-components/experiment-trackers/wandb.md): Logging and visualizing experiments with Weights & Biases. - [Google Cloud VertexAI Experiment Tracker](https://docs.zenml.io/stacks/stack-components/experiment-trackers/vertexai.md): Logging and visualizing experiments with Vertex AI Experiment Tracker. - [Develop a custom experiment tracker](https://docs.zenml.io/stacks/stack-components/experiment-trackers/custom.md): Learning how to develop a custom experiment tracker. - [Image Builders](https://docs.zenml.io/stacks/stack-components/image-builders.md): Building container images for your ML workflow. - [Local Image Builder](https://docs.zenml.io/stacks/stack-components/image-builders/local.md): Building container images locally. - [Kaniko Image Builder](https://docs.zenml.io/stacks/stack-components/image-builders/kaniko.md): Building container images with Kaniko. - [AWS Image Builder](https://docs.zenml.io/stacks/stack-components/image-builders/aws.md): Building container images with AWS CodeBuild - [Google Cloud Image Builder](https://docs.zenml.io/stacks/stack-components/image-builders/gcp.md): Building container images with Google Cloud Build - [Develop a Custom Image Builder](https://docs.zenml.io/stacks/stack-components/image-builders/custom.md): Learning how to develop a custom image builder. - [Alerters](https://docs.zenml.io/stacks/stack-components/alerters.md): Sending automated alerts to chat services. - [Discord Alerter](https://docs.zenml.io/stacks/stack-components/alerters/discord.md): Sending automated alerts to a Discord channel. - [Slack Alerter](https://docs.zenml.io/stacks/stack-components/alerters/slack.md): Sending automated alerts to a Slack channel. - [Develop a Custom Alerter](https://docs.zenml.io/stacks/stack-components/alerters/custom.md): Learning how to develop a custom alerter. - [Annotators](https://docs.zenml.io/stacks/stack-components/annotators.md): Annotating the data in your workflow. - [Argilla](https://docs.zenml.io/stacks/stack-components/annotators/argilla.md): Annotating data using Argilla. - [Label Studio](https://docs.zenml.io/stacks/stack-components/annotators/label-studio.md): Annotating data using Label Studio. - [Pigeon](https://docs.zenml.io/stacks/stack-components/annotators/pigeon.md): Annotating data using Pigeon. - [Prodigy](https://docs.zenml.io/stacks/stack-components/annotators/prodigy.md): Annotating data using Prodigy. - [Develop a Custom Annotator](https://docs.zenml.io/stacks/stack-components/annotators/custom.md): Learning how to develop a custom annotator. - [Data Validators](https://docs.zenml.io/stacks/stack-components/data-validators.md): 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/stack-components/data-validators/great-expectations.md): How to use Great Expectations to run data quality checks in your pipelines and document the results - [Deepchecks](https://docs.zenml.io/stacks/stack-components/data-validators/deepchecks.md): How to test the data and models used in your pipelines with Deepchecks test suites - [Evidently](https://docs.zenml.io/stacks/stack-components/data-validators/evidently.md): How to keep your data quality in check and guard against data and model drift with Evidently profiling - [Whylogs](https://docs.zenml.io/stacks/stack-components/data-validators/whylogs.md): 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/stack-components/data-validators/custom.md): How to develop a custom data validator - [Feature Stores](https://docs.zenml.io/stacks/stack-components/feature-stores.md): Managing data in feature stores. - [Feast](https://docs.zenml.io/stacks/stack-components/feature-stores/feast.md): Managing data in Feast feature stores. - [Develop a Custom Feature Store](https://docs.zenml.io/stacks/stack-components/feature-stores/custom.md): Learning how to develop a custom feature store. - [Model Deployers](https://docs.zenml.io/stacks/stack-components/model-deployers.md): Deploying your models and serve real-time predictions. - [MLflow](https://docs.zenml.io/stacks/stack-components/model-deployers/mlflow.md): Deploying your models locally with MLflow. - [Seldon](https://docs.zenml.io/stacks/stack-components/model-deployers/seldon.md): Deploying models to Kubernetes with Seldon Core. - [BentoML](https://docs.zenml.io/stacks/stack-components/model-deployers/bentoml.md): Deploying your models locally with BentoML. - [Hugging Face](https://docs.zenml.io/stacks/stack-components/model-deployers/huggingface.md): Deploying models to Huggingface Inference Endpoints with Hugging Face :hugging\_face:. - [Databricks](https://docs.zenml.io/stacks/stack-components/model-deployers/databricks.md): Deploying models to Databricks Inference Endpoints with Databricks - [vLLM](https://docs.zenml.io/stacks/stack-components/model-deployers/vllm.md): Deploying your LLM locally with vLLM. - [Develop a Custom Model Deployer](https://docs.zenml.io/stacks/stack-components/model-deployers/custom.md): Learning how to develop a custom model deployer. - [Model Registries](https://docs.zenml.io/stacks/stack-components/model-registries.md): Tracking and managing ML models. - [MLflow Model Registry](https://docs.zenml.io/stacks/stack-components/model-registries/mlflow.md): Managing MLFlow logged models and artifacts - [Develop a Custom Model Registry](https://docs.zenml.io/stacks/stack-components/model-registries/custom.md): Learning how to develop a custom model registry. --- # Agent Instructions: Querying This Documentation If you need additional information that is not directly available in this page, you can query the documentation dynamically by asking a question. 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