Datasets:

zenml
/

llms.txt

@@ -157,11 +157,6 @@ description: Sending automated alerts to chat services.
 icon: message-exclamation
 ---
-{% hint style="warning" %}
-This is an older version of the ZenML documentation. To read and view the latest version please [visit this up-to-date URL](https://docs.zenml.io).
-{% endhint %}
 # Alerters
 **Alerters** allow you to send messages to chat services (like Slack, Discord, Mattermost, etc.) from within your
@@ -217,11 +212,6 @@ File: docs/book/component-guide/alerters/custom.md
 description: Learning how to develop a custom alerter.
 ---
-{% hint style="warning" %}
-This is an older version of the ZenML documentation. To read and view the latest version please [visit this up-to-date URL](https://docs.zenml.io).
-{% endhint %}
 # Develop a Custom Alerter
 {% hint style="info" %}
@@ -369,11 +359,6 @@ File: docs/book/component-guide/alerters/discord.md
 description: Sending automated alerts to a Discord channel.
 ---
-{% hint style="warning" %}
-This is an older version of the ZenML documentation. To read and view the latest version please [visit this up-to-date URL](https://docs.zenml.io).
-{% endhint %}
 # Discord Alerter
 The `DiscordAlerter` enables you to send messages to a dedicated Discord channel
@@ -516,11 +501,6 @@ File: docs/book/component-guide/alerters/slack.md
 description: Sending automated alerts to a Slack channel.
 ---
-{% hint style="warning" %}
-This is an older version of the ZenML documentation. To read and view the latest version please [visit this up-to-date URL](https://docs.zenml.io).
-{% endhint %}
 # Slack Alerter
 The `SlackAlerter` enables you to send messages or ask questions within a
@@ -862,11 +842,6 @@ File: docs/book/component-guide/annotators/argilla.md
 description: Annotating data using Argilla.
 ---
-{% hint style="warning" %}
-This is an older version of the ZenML documentation. To read and view the latest version please [visit this up-to-date URL](https://docs.zenml.io).
-{% endhint %}
 # Argilla
 [Argilla](https://github.com/argilla-io/argilla) is a collaboration tool for AI engineers and domain experts who need to build high-quality datasets for their projects. It enables users to build robust language models through faster data curation using both human and machine feedback, providing support for each step in the MLOps cycle, from data labeling to model monitoring.
@@ -1010,11 +985,6 @@ File: docs/book/component-guide/annotators/custom.md
 description: Learning how to develop a custom annotator.
 ---
-{% hint style="warning" %}
-This is an older version of the ZenML documentation. To read and view the latest version please [visit this up-to-date URL](https://docs.zenml.io).
-{% endhint %}
 # Develop a Custom Annotator
 {% hint style="info" %}
@@ -1038,11 +1008,6 @@ File: docs/book/component-guide/annotators/label-studio.md
 description: Annotating data using Label Studio.
 ---
-{% hint style="warning" %}
-This is an older version of the ZenML documentation. To read and view the latest version please [visit this up-to-date URL](https://docs.zenml.io).
-{% endhint %}
 # Label Studio
 Label Studio is one of the leading open-source annotation platforms available to data scientists and ML practitioners.
@@ -1195,11 +1160,6 @@ File: docs/book/component-guide/annotators/pigeon.md
 description: Annotating data using Pigeon.
 ---
-{% hint style="warning" %}
-This is an older version of the ZenML documentation. To read and view the latest version please [visit this up-to-date URL](https://docs.zenml.io).
-{% endhint %}
 # Pigeon
 Pigeon is a lightweight, open-source annotation tool designed for quick and easy labeling of data directly within Jupyter notebooks. It provides a simple and intuitive interface for annotating various types of data, including:
@@ -1317,11 +1277,6 @@ File: docs/book/component-guide/annotators/prodigy.md
 description: Annotating data using Prodigy.
 ---
-{% hint style="warning" %}
-This is an older version of the ZenML documentation. To read and view the latest version please [visit this up-to-date URL](https://docs.zenml.io).
-{% endhint %}
 # Prodigy
 [Prodigy](https://prodi.gy/) is a modern annotation tool for creating training
@@ -1461,11 +1416,6 @@ description: Setting up a persistent storage for your artifacts.
 icon: folder-closed
 ---
-{% hint style="warning" %}
-This is an older version of the ZenML documentation. To read and view the latest version please [visit this up-to-date URL](https://docs.zenml.io).
-{% endhint %}
 # Artifact Stores
 The Artifact Store is a central component in any MLOps stack. As the name suggests, it acts as a data persistence layer where artifacts (e.g. datasets, models) ingested or generated by the machine learning pipelines are stored.
@@ -1638,11 +1588,6 @@ File: docs/book/component-guide/artifact-stores/azure.md
 description: Storing artifacts using Azure Blob Storage
 ---
-{% hint style="warning" %}
-This is an older version of the ZenML documentation. To read and view the latest version please [visit this up-to-date URL](https://docs.zenml.io).
-{% endhint %}
 # Azure Blob Storage
 The Azure Artifact Store is an [Artifact Store](./artifact-stores.md) flavor provided with the Azure ZenML integration that uses [the Azure Blob Storage managed object storage service](https://azure.microsoft.com/en-us/services/storage/blobs/) to store ZenML artifacts in an Azure Blob Storage container.
@@ -1875,11 +1820,6 @@ File: docs/book/component-guide/artifact-stores/custom.md
 description: Learning how to develop a custom artifact store.
 ---
-{% hint style="warning" %}
-This is an older version of the ZenML documentation. To read and view the latest version please [visit this up-to-date URL](https://docs.zenml.io).
-{% endhint %}
 # Develop a custom artifact store
 {% hint style="info" %}
@@ -2072,11 +2012,6 @@ File: docs/book/component-guide/artifact-stores/gcp.md
 description: Storing artifacts using GCP Cloud Storage.
 ---
-{% hint style="warning" %}
-This is an older version of the ZenML documentation. To read and view the latest version please [visit this up-to-date URL](https://docs.zenml.io).
-{% endhint %}
 # Google Cloud Storage (GCS)
 The GCS Artifact Store is an [Artifact Store](./artifact-stores.md) flavor provided with the GCP ZenML integration that uses [the Google Cloud Storage managed object storage service](https://cloud.google.com/storage/docs/introduction) to store ZenML artifacts in a GCP Cloud Storage bucket.
@@ -2281,11 +2216,6 @@ File: docs/book/component-guide/artifact-stores/local.md
 description: Storing artifacts on your local filesystem.
 ---
-{% hint style="warning" %}
-This is an older version of the ZenML documentation. To read and view the latest version please [visit this up-to-date URL](https://docs.zenml.io).
-{% endhint %}
 # Local Artifact Store
 The local Artifact Store is a built-in ZenML [Artifact Store](./artifact-stores.md) flavor that uses a folder on your local filesystem to store artifacts.
@@ -2374,11 +2304,6 @@ File: docs/book/component-guide/artifact-stores/s3.md
 description: Storing artifacts in an AWS S3 bucket.
 ---
-{% hint style="warning" %}
-This is an older version of the ZenML documentation. To read and view the latest version please [visit this up-to-date URL](https://docs.zenml.io).
-{% endhint %}
 # Amazon Simple Cloud Storage (S3)
 The S3 Artifact Store is an [Artifact Store](./artifact-stores.md) flavor provided with the S3 ZenML integration that uses [the AWS S3 managed object storage service](https://aws.amazon.com/s3/) or one of the self-hosted S3 alternatives, such as [MinIO](https://min.io/) or [Ceph RGW](https://ceph.io/en/discover/technology/#object), to store artifacts in an S3 compatible object storage backend.
@@ -2603,11 +2528,6 @@ File: docs/book/component-guide/container-registries/aws.md
 description: Storing container images in Amazon ECR.
 ---
-{% hint style="warning" %}
-This is an older version of the ZenML documentation. To read and view the latest version please [visit this up-to-date URL](https://docs.zenml.io).
-{% endhint %}
 # Amazon Elastic Container Registry (ECR)
 The AWS container registry is a [container registry](./container-registries.md) flavor provided with the ZenML `aws` integration and uses [Amazon ECR](https://aws.amazon.com/ecr/) to store container images.
@@ -2819,11 +2739,6 @@ File: docs/book/component-guide/container-registries/azure.md
 description: Storing container images in Azure.
 ---
-{% hint style="warning" %}
-This is an older version of the ZenML documentation. To read and view the latest version please [visit this up-to-date URL](https://docs.zenml.io).
-{% endhint %}
 # Azure Container Registry
 The Azure container registry is a [container registry](./container-registries.md) flavor that comes built-in with ZenML and uses the [Azure Container Registry](https://azure.microsoft.com/en-us/services/container-registry/) to store container images.
@@ -3078,11 +2993,6 @@ File: docs/book/component-guide/container-registries/custom.md
 description: Learning how to develop a custom container registry.
 ---
-{% hint style="warning" %}
-This is an older version of the ZenML documentation. To read and view the latest version please [visit this up-to-date URL](https://docs.zenml.io).
-{% endhint %}
 # Develop a custom container registry
 {% hint style="info" %}
@@ -3209,11 +3119,6 @@ File: docs/book/component-guide/container-registries/default.md
 description: Storing container images locally.
 ---
-{% hint style="warning" %}
-This is an older version of the ZenML documentation. To read and view the latest version please [visit this up-to-date URL](https://docs.zenml.io).
-{% endhint %}
 # Default Container Registry
 The Default container registry is a [container registry](./container-registries.md) flavor that comes built-in with ZenML and allows container registry URIs of any format.
@@ -3391,11 +3296,6 @@ File: docs/book/component-guide/container-registries/dockerhub.md
 description: Storing container images in DockerHub.
 ---
-{% hint style="warning" %}
-This is an older version of the ZenML documentation. To read and view the latest version please [visit this up-to-date URL](https://docs.zenml.io).
-{% endhint %}
 # DockerHub
 The DockerHub container registry is a [container registry](./container-registries.md) flavor that comes built-in with ZenML and uses [DockerHub](https://hub.docker.com/) to store container images.
@@ -3469,11 +3369,6 @@ File: docs/book/component-guide/container-registries/gcp.md
 description: Storing container images in GCP.
 ---
-{% hint style="warning" %}
-This is an older version of the ZenML documentation. To read and view the latest version please [visit this up-to-date URL](https://docs.zenml.io).
-{% endhint %}
 # Google Cloud Container Registry
 The GCP container registry is a [container registry](./container-registries.md) flavor that comes built-in with ZenML and uses the [Google Artifact Registry](https://cloud.google.com/artifact-registry).
@@ -3570,13 +3465,9 @@ Stacks using the GCP Container Registry set up with local authentication are not
 {% endtab %}
 {% tab title="GCP Service Connector (recommended)" %}
-To set up the GCP Container Registry to authenticate to GCP and access a GCR registry, it is recommended to leverage the many features provided by [the GCP Service Connector](../../how-to/infrastructure-deployment/auth-management/gcp-service-connector.md) such as auto-configuration, local login, best security practices regarding long-lived credentials and reusing the same credentials across multiple stack components.
-{% hint style="warning" %}
-The GCP Service Connector does not support the Google Artifact Registry yet. If you need to connect your GCP Container Registry to a Google Artifact Registry, you can use the _Local Authentication_ method instead.
-{% endhint %}
-If you don't already have a GCP Service Connector configured in your ZenML deployment, you can register one using the interactive CLI command. You have the option to configure a GCP Service Connector that can be used to access a GCR registry or even more than one type of GCP resource:
 ```sh
 zenml service-connector register --type gcp -i
@@ -3715,11 +3606,6 @@ File: docs/book/component-guide/container-registries/github.md
 description: Storing container images in GitHub.
 ---
-{% hint style="warning" %}
-This is an older version of the ZenML documentation. To read and view the latest version please [visit this up-to-date URL](https://docs.zenml.io).
-{% endhint %}
 # GitHub Container Registry
 The GitHub container registry is a [container registry](./container-registries.md) flavor that comes built-in with ZenML and uses the [GitHub Container Registry](https://docs.github.com/en/packages/working-with-a-github-packages-registry/working-with-the-container-registry) to store container images.
@@ -3782,11 +3668,6 @@ File: docs/book/component-guide/data-validators/custom.md
 description: How to develop a custom data validator
 ---
-{% hint style="warning" %}
-This is an older version of the ZenML documentation. To read and view the latest version please [visit this up-to-date URL](https://docs.zenml.io).
-{% endhint %}
 # Develop a custom data validator
 {% hint style="info" %}
@@ -3916,11 +3797,6 @@ description: >-
   suites
 ---
-{% hint style="warning" %}
-This is an older version of the ZenML documentation. To read and view the latest version please [visit this up-to-date URL](https://docs.zenml.io).
-{% endhint %}
 # Deepchecks
 The Deepchecks [Data Validator](./data-validators.md) flavor provided with the ZenML integration uses [Deepchecks](https://deepchecks.com/) to run data integrity, data drift, model drift and model performance tests on the datasets and models circulated in your ZenML pipelines. The test results can be used to implement automated corrective actions in your pipelines or to render interactive representations for further visual interpretation, evaluation and documentation.
@@ -4345,11 +4221,6 @@ description: >-
   with Evidently profiling
 ---
-{% hint style="warning" %}
-This is an older version of the ZenML documentation. To read and view the latest version please [visit this up-to-date URL](https://docs.zenml.io).
-{% endhint %}
 # Evidently
 The Evidently [Data Validator](./data-validators.md) flavor provided with the ZenML integration uses [Evidently](https://evidentlyai.com/) to perform data quality, data drift, model drift and model performance analyzes, to generate reports and run checks. The reports and check results can be used to implement automated corrective actions in your pipelines or to render interactive representations for further visual interpretation, evaluation and documentation.
@@ -4987,11 +4858,6 @@ description: >-
   document the results
 ---
-{% hint style="warning" %}
-This is an older version of the ZenML documentation. To read and view the latest version please [visit this up-to-date URL](https://docs.zenml.io).
-{% endhint %}
 # Great Expectations
 The Great Expectations [Data Validator](./data-validators.md) flavor provided with the ZenML integration uses [Great Expectations](https://greatexpectations.io/) to run data profiling and data quality tests on the data circulated through your pipelines. The test results can be used to implement automated corrective actions in your pipelines. They are also automatically rendered into documentation for further visual interpretation and evaluation.
@@ -5304,11 +5170,6 @@ description: >-
   data with whylogs/WhyLabs profiling.
 ---
-{% hint style="warning" %}
-This is an older version of the ZenML documentation. To read and view the latest version please [visit this up-to-date URL](https://docs.zenml.io).
-{% endhint %}
 # Whylogs
 The whylogs/WhyLabs [Data Validator](./data-validators.md) flavor provided with the ZenML integration uses [whylogs](https://whylabs.ai/whylogs) and [WhyLabs](https://whylabs.ai) to generate and track data profiles, highly accurate descriptive representations of your data. The profiles can be used to implement automated corrective actions in your pipelines, or to render interactive representations for further visual interpretation, evaluation and documentation.
@@ -5596,11 +5457,6 @@ File: docs/book/component-guide/experiment-trackers/comet.md
 description: Logging and visualizing experiments with Comet.
 ---
-{% hint style="warning" %}
-This is an older version of the ZenML documentation. To read and view the latest version please [visit this up-to-date URL](https://docs.zenml.io).
-{% endhint %}
 # Comet
 The Comet Experiment Tracker is an [Experiment Tracker](./experiment-trackers.md) flavor provided with the Comet ZenML integration that uses [the Comet experiment tracking platform](https://www.comet.com/site/products/ml-experiment-tracking/) to log and visualize information from your pipeline steps (e.g., models, parameters, metrics).
@@ -5896,11 +5752,6 @@ File: docs/book/component-guide/experiment-trackers/custom.md
 description: Learning how to develop a custom experiment tracker.
 ---
-{% hint style="warning" %}
-This is an older version of the ZenML documentation. To read and view the latest version please [visit this up-to-date URL](https://docs.zenml.io).
-{% endhint %}
 # Develop a custom experiment tracker
 {% hint style="info" %}
@@ -5967,11 +5818,6 @@ description: Logging and visualizing ML experiments.
 icon: clipboard
 ---
-{% hint style="warning" %}
-This is an older version of the ZenML documentation. To read and view the latest version please [visit this up-to-date URL](https://docs.zenml.io).
-{% endhint %}
 # Experiment Trackers
 Experiment trackers let you track your ML experiments by logging extended information about your models, datasets,
@@ -6065,11 +5911,6 @@ File: docs/book/component-guide/experiment-trackers/mlflow.md
 description: Logging and visualizing experiments with MLflow.
 ---
-{% hint style="warning" %}
-This is an older version of the ZenML documentation. To read and view the latest version please [visit this up-to-date URL](https://docs.zenml.io).
-{% endhint %}
 # MLflow
 The MLflow Experiment Tracker is an [Experiment Tracker](./experiment-trackers.md) flavor provided with the MLflow ZenML integration that uses [the MLflow tracking service](https://mlflow.org/docs/latest/tracking.html) to log and visualize information from your pipeline steps (e.g. models, parameters, metrics).
@@ -6288,11 +6129,6 @@ File: docs/book/component-guide/experiment-trackers/neptune.md
 description: Logging and visualizing experiments with neptune.ai
 ---
-{% hint style="warning" %}
-This is an older version of the ZenML documentation. To read and view the latest version please [visit this up-to-date URL](https://docs.zenml.io).
-{% endhint %}
 # Neptune
 The Neptune Experiment Tracker is an [Experiment Tracker](./experiment-trackers.md) flavor provided with the Neptune-ZenML integration that uses [neptune.ai](https://neptune.ai/product/experiment-tracking) to log and visualize information from your pipeline steps (e.g. models, parameters, metrics).
@@ -6611,11 +6447,6 @@ File: docs/book/component-guide/experiment-trackers/vertexai.md
 description: Logging and visualizing experiments with Vertex AI Experiment Tracker.
 ---
-{% hint style="warning" %}
-This is an older version of the ZenML documentation. To read and view the latest version please [visit this up-to-date URL](https://docs.zenml.io).
-{% endhint %}
 # Vertex AI Experiment Tracker
 The Vertex AI Experiment Tracker is an [Experiment Tracker](./experiment-trackers.md) flavor provided with the Vertex AI ZenML integration. It uses the [Vertex AI tracking service](https://cloud.google.com/vertex-ai/docs/experiments/intro-vertex-ai-experiments) to log and visualize information from your pipeline steps (e.g., models, parameters, metrics).
@@ -6935,11 +6766,6 @@ File: docs/book/component-guide/experiment-trackers/wandb.md
 description: Logging and visualizing experiments with Weights & Biases.
 ---
-{% hint style="warning" %}
-This is an older version of the ZenML documentation. To read and view the latest version please [visit this up-to-date URL](https://docs.zenml.io).
-{% endhint %}
 # Weights & Biases
 The Weights & Biases Experiment Tracker is an [Experiment Tracker](./experiment-trackers.md) flavor provided with the Weights & Biases ZenML integration that uses [the Weights & Biases experiment tracking platform](https://wandb.ai/site/experiment-tracking) to log and visualize information from your pipeline steps (e.g. models, parameters, metrics).
@@ -7257,11 +7083,6 @@ File: docs/book/component-guide/feature-stores/custom.md
 description: Learning how to develop a custom feature store.
 ---
-{% hint style="warning" %}
-This is an older version of the ZenML documentation. To read and view the latest version please [visit this up-to-date URL](https://docs.zenml.io).
-{% endhint %}
 # Develop a Custom Feature Store
 {% hint style="info" %}
@@ -7285,11 +7106,6 @@ File: docs/book/component-guide/feature-stores/feast.md
 description: Managing data in Feast feature stores.
 ---
-{% hint style="warning" %}
-This is an older version of the ZenML documentation. To read and view the latest version please [visit this up-to-date URL](https://docs.zenml.io).
-{% endhint %}
 # Feast
 Feast (Feature Store) is an operational data system for managing and serving machine learning features to models in production. Feast is able to serve feature data to models from a low-latency online store (for real-time prediction) or from an offline store (for scale-out batch scoring or model training).
@@ -7472,11 +7288,6 @@ File: docs/book/component-guide/image-builders/aws.md
 description: Building container images with AWS CodeBuild
 ---
-{% hint style="warning" %}
-This is an older version of the ZenML documentation. To read and view the latest version please [visit this up-to-date URL](https://docs.zenml.io).
-{% endhint %}
 # AWS Image Builder
 The AWS image builder is an [image builder](./image-builders.md) flavor provided by the ZenML `aws` integration that uses [AWS CodeBuild](https://aws.amazon.com/codebuild) to build container images.
@@ -7715,11 +7526,6 @@ File: docs/book/component-guide/image-builders/custom.md
 description: Learning how to develop a custom image builder.
 ---
-{% hint style="warning" %}
-This is an older version of the ZenML documentation. To read and view the latest version please [visit this up-to-date URL](https://docs.zenml.io).
-{% endhint %}
 # Develop a Custom Image Builder
 {% hint style="info" %}
@@ -7840,11 +7646,6 @@ File: docs/book/component-guide/image-builders/gcp.md
 description: Building container images with Google Cloud Build
 ---
-{% hint style="warning" %}
-This is an older version of the ZenML documentation. To read and view the latest version please [visit this up-to-date URL](https://docs.zenml.io).
-{% endhint %}
 # Google Cloud Image Builder
 The Google Cloud image builder is an [image builder](./image-builders.md) flavor provided by the ZenML `gcp` integration that uses [Google Cloud Build](https://cloud.google.com/build) to build container images.
@@ -8098,11 +7899,6 @@ File: docs/book/component-guide/image-builders/kaniko.md
 description: Building container images with Kaniko.
 ---
-{% hint style="warning" %}
-This is an older version of the ZenML documentation. To read and view the latest version please [visit this up-to-date URL](https://docs.zenml.io).
-{% endhint %}
 # Kaniko Image Builder
 The Kaniko image builder is an [image builder](./image-builders.md) flavor provided by the ZenML `kaniko` integration that uses [Kaniko](https://github.com/GoogleContainerTools/kaniko) to build container images.
@@ -8244,7 +8040,7 @@ List of some possible additional flags:
 * `--cache`: Set to `false` to disable caching. Defaults to `true`.
 * `--cache-dir`: Set the directory where to store cached layers. Defaults to `/cache`.
-* `--cache-repo`: Set the repository where to store cached layers. Defaults to `gcr.io/kaniko-project/executor`.
 * `--cache-ttl`: Set the cache expiration time. Defaults to `24h`.
 * `--cleanup`: Set to `false` to disable cleanup of the working directory. Defaults to `true`.
 * `--compressed-caching`: Set to `false` to disable compressed caching. Defaults to `true`.
@@ -8260,11 +8056,6 @@ File: docs/book/component-guide/image-builders/local.md
 description: Building container images locally.
 ---
-{% hint style="warning" %}
-This is an older version of the ZenML documentation. To read and view the latest version please [visit this up-to-date URL](https://docs.zenml.io).
-{% endhint %}
 # Local Image Builder
 The local image builder is an [image builder](./image-builders.md) flavor that comes built-in with ZenML and uses the local Docker installation on your client machine to build container images.
@@ -8317,11 +8108,6 @@ File: docs/book/component-guide/model-deployers/bentoml.md
 description: Deploying your models locally with BentoML.
 ---
-{% hint style="warning" %}
-This is an older version of the ZenML documentation. To read and view the latest version please [visit this up-to-date URL](https://docs.zenml.io).
-{% endhint %}
 # BentoML
 BentoML is an open-source framework for machine learning model serving. it can be used to deploy models locally, in a cloud environment, or in a Kubernetes environment.
@@ -8369,7 +8155,7 @@ The recommended flow to use the BentoML model deployer is to first [create a Ben
 ### Create a BentoML Service
-The first step to being able to deploy your models and use BentoML is to create a [bento service](https://docs.bentoml.com/en/latest/guides/services.html) which is the main logic that defines how your model will be served. The
 The following example shows how to create a basic bento service that will be used to serve a torch model. Learn more about how to specify the inputs and outputs for the APIs and how to use validators in the [Input and output types BentoML docs](https://docs.bentoml.com/en/latest/guides/iotypes.html)
@@ -8708,11 +8494,6 @@ File: docs/book/component-guide/model-deployers/custom.md
 description: Learning how to develop a custom model deployer.
 ---
-{% hint style="warning" %}
-This is an older version of the ZenML documentation. To read and view the latest version please [visit this up-to-date URL](https://docs.zenml.io).
-{% endhint %}
 # Develop a Custom Model Deployer
 {% hint style="info" %}
@@ -8885,11 +8666,6 @@ description: >-
   Deploying models to Databricks Inference Endpoints with Databricks
 ---
-{% hint style="warning" %}
-This is an older version of the ZenML documentation. To read and view the latest version please [visit this up-to-date URL](https://docs.zenml.io).
-{% endhint %}
 # Databricks
@@ -9043,11 +8819,6 @@ description: >-
   :hugging_face:.
 ---
-{% hint style="warning" %}
-This is an older version of the ZenML documentation. To read and view the latest version please [visit this up-to-date URL](https://docs.zenml.io).
-{% endhint %}
 # Hugging Face
 Hugging Face Inference Endpoints provides a secure production solution to easily deploy any `transformers`, `sentence-transformers`, and `diffusers` models on a dedicated and autoscaling infrastructure managed by Hugging Face. An Inference Endpoint is built from a model from the [Hub](https://huggingface.co/models).
@@ -9240,11 +9011,6 @@ File: docs/book/component-guide/model-deployers/mlflow.md
 description: Deploying your models locally with MLflow.
 ---
-{% hint style="warning" %}
-This is an older version of the ZenML documentation. To read and view the latest version please [visit this up-to-date URL](https://docs.zenml.io).
-{% endhint %}
 # MLflow
 The MLflow Model Deployer is one of the available flavors of the [Model Deployer](./model-deployers.md) stack component. Provided with the MLflow integration it can be used to deploy and manage [MLflow models](https://www.mlflow.org/docs/latest/python\_api/mlflow.deployments.html) on a local running MLflow server.
@@ -9568,13 +9334,13 @@ zenml model-deployer register seldon --flavor=seldon \
    ```
 * Lifecycle Management: Provides mechanisms for comprehensive lifecycle management of model servers, including the ability to start, stop, and delete model servers, as well as to update existing servers with new model versions, thereby optimizing resource utilization and facilitating continuous delivery of model updates. Some core methods that can be used to interact with the remote model server include:
-`deploy_model` - Deploys a model to the serving environment and returns a Service object that represents the deployed model server.
-`find_model_server` - Finds and returns a list of Service objects that represent model servers that have been deployed to the serving environment, the
-services are stored in the DB and can be used as a reference to know what and where the model is deployed.
-`stop_model_server` - Stops a model server that is currently running in the serving environment.
-`start_model_server` - Starts a model server that has been stopped in the serving environment.
-`delete_model_server` - Deletes a model server from the serving environment and from the DB.
 {% hint style="info" %}
 ZenML uses the Service object to represent a model server that has been deployed to a serving environment. The Service object is saved in the DB and can be used as a reference to know what and where the model is deployed. The Service object consists of 2 main attributes, the `config` and the `status`. The `config` attribute holds all the deployment configuration attributes required to create a new deployment, while the `status` attribute holds the operational status of the deployment, such as the last error message, the prediction URL, and the deployment status.
@@ -9689,11 +9455,6 @@ File: docs/book/component-guide/model-deployers/seldon.md
 description: Deploying models to Kubernetes with Seldon Core.
 ---
-{% hint style="warning" %}
-This is an older version of the ZenML documentation. To read and view the latest version please [visit this up-to-date URL](https://docs.zenml.io).
-{% endhint %}
 # Seldon
 [Seldon Core](https://github.com/SeldonIO/seldon-core) is a production grade source-available model serving platform. It packs a wide range of features built around deploying models to REST/GRPC microservices that include monitoring and logging, model explainers, outlier detectors and various continuous deployment strategies such as A/B testing, canary deployments and more.
@@ -10173,11 +9934,6 @@ File: docs/book/component-guide/model-deployers/vllm.md
 description: Deploying your LLM locally with vLLM.
 ---
-{% hint style="warning" %}
-This is an older version of the ZenML documentation. To read and view the latest version please [visit this up-to-date URL](https://docs.zenml.io).
-{% endhint %}
 # vLLM
 [vLLM](https://docs.vllm.ai/en/latest/) is a fast and easy-to-use library for LLM inference and serving.
@@ -10256,11 +10012,6 @@ File: docs/book/component-guide/model-registries/custom.md
 description: Learning how to develop a custom model registry.
 ---
-{% hint style="warning" %}
-This is an older version of the ZenML documentation. To read and view the latest version please [visit this up-to-date URL](https://docs.zenml.io).
-{% endhint %}
 # Develop a Custom Model Registry
 {% hint style="info" %}
@@ -10457,11 +10208,6 @@ File: docs/book/component-guide/model-registries/mlflow.md
 description: Managing MLFlow logged models and artifacts
 ---
-{% hint style="warning" %}
-This is an older version of the ZenML documentation. To read and view the latest version please [visit this up-to-date URL](https://docs.zenml.io).
-{% endhint %}
 # MLflow Model Registry
 [MLflow](https://www.mlflow.org/docs/latest/tracking.html) is a popular tool that helps you track experiments, manage models and even deploy them to different environments. ZenML already provides a [MLflow Experiment Tracker](../experiment-trackers/mlflow.md) that you can use to track your experiments, and an [MLflow Model Deployer](../model-deployers/mlflow.md) that you can use to deploy your models locally.
@@ -10711,11 +10457,6 @@ File: docs/book/component-guide/orchestrators/airflow.md
 description: Orchestrating your pipelines to run on Airflow.
 ---
-{% hint style="warning" %}
-This is an older version of the ZenML documentation. To read and view the latest version please [visit this up-to-date URL](https://docs.zenml.io).
-{% endhint %}
 # Airflow Orchestrator
 ZenML pipelines can be executed natively as [Airflow](https://airflow.apache.org/)
@@ -11025,11 +10766,6 @@ File: docs/book/component-guide/orchestrators/azureml.md
 description: Orchestrating your pipelines to run on AzureML.
 ---
-{% hint style="warning" %}
-This is an older version of the ZenML documentation. To read and view the latest version please [visit this up-to-date URL](https://docs.zenml.io).
-{% endhint %}
 # AzureML Orchestrator
 [AzureML](https://azure.microsoft.com/en-us/products/machine-learning) is a
@@ -11276,11 +11012,6 @@ File: docs/book/component-guide/orchestrators/custom.md
 description: Learning how to develop a custom orchestrator.
 ---
-{% hint style="warning" %}
-This is an older version of the ZenML documentation. To read and view the latest version please [visit this up-to-date URL](https://docs.zenml.io).
-{% endhint %}
 # Develop a custom orchestrator
 {% hint style="info" %}
@@ -11505,11 +11236,6 @@ File: docs/book/component-guide/orchestrators/databricks.md
 description: Orchestrating your pipelines to run on Databricks.
 ---
-{% hint style="warning" %}
-This is an older version of the ZenML documentation. To read and view the latest version please [visit this up-to-date URL](https://docs.zenml.io).
-{% endhint %}
 # Databricks Orchestrator
 [Databricks](https://www.databricks.com/) is a unified data analytics platform that combines the best of data warehouses and data lakes to offer an integrated solution for big data processing and machine learning. It provides a collaborative environment for data scientists, data engineers, and business analysts to work together on data projects. Databricks offers optimized performance and scalability for big data workloads.
@@ -11706,11 +11432,6 @@ File: docs/book/component-guide/orchestrators/hyperai.md
 description: Orchestrating your pipelines to run on HyperAI.ai instances.
 ---
-{% hint style="warning" %}
-This is an older version of the ZenML documentation. To read and view the latest version please [visit this up-to-date URL](https://docs.zenml.io).
-{% endhint %}
 # HyperAI Orchestrator
 [HyperAI](https://www.hyperai.ai) is a cutting-edge cloud compute platform designed to make AI accessible for everyone. The HyperAI orchestrator is an [orchestrator](./orchestrators.md) flavor that allows you to easily deploy your pipelines on HyperAI instances.
@@ -11798,11 +11519,6 @@ File: docs/book/component-guide/orchestrators/kubeflow.md
 description: Orchestrating your pipelines to run on Kubeflow.
 ---
-{% hint style="warning" %}
-This is an older version of the ZenML documentation. To read and view the latest version please [visit this up-to-date URL](https://docs.zenml.io).
-{% endhint %}
 # Kubeflow Orchestrator
 The Kubeflow orchestrator is an [orchestrator](./orchestrators.md) flavor provided by the ZenML `kubeflow` integration that uses [Kubeflow Pipelines](https://www.kubeflow.org/docs/components/pipelines/overview/) to run your pipelines.
@@ -12160,11 +11876,6 @@ File: docs/book/component-guide/orchestrators/kubernetes.md
 description: Orchestrating your pipelines to run on Kubernetes clusters.
 ---
-{% hint style="warning" %}
-This is an older version of the ZenML documentation. To read and view the latest version please [visit this up-to-date URL](https://docs.zenml.io).
-{% endhint %}
 # Kubernetes Orchestrator
 Using the ZenML `kubernetes` integration, you can orchestrate and scale your ML pipelines on a [Kubernetes](https://kubernetes.io/) cluster without writing a single line of Kubernetes code.
@@ -12173,9 +11884,7 @@ This Kubernetes-native orchestrator is a minimalist, lightweight alternative to
 Overall, the Kubernetes orchestrator is quite similar to the Kubeflow orchestrator in that it runs each pipeline step in a separate Kubernetes pod. However, the orchestration of the different pods is not done by Kubeflow but by a separate master pod that orchestrates the step execution via topological sort.
-Compared to Kubeflow, this means that the Kubernetes-native orchestrator is faster and much simpler to start with since you do not need to install and maintain Kubeflow on your cluster. The Kubernetes-native orchestrator is an ideal choice for teams new to distributed orchestration that do not want to go with a fully-managed offering.
-However, since Kubeflow is much more mature, you should, in most cases, aim to move your pipelines to Kubeflow in the long run. A smooth way to production-grade orchestration could be to set up a Kubernetes cluster first and get started with the Kubernetes-native orchestrator. If needed, you can then install and set up Kubeflow later and simply switch out the orchestrator of your stack as soon as your full setup is ready.
 {% hint style="warning" %}
 This component is only meant to be used within the context of a [remote ZenML deployment scenario](../../getting-started/deploying-zenml/README.md). Usage with a local ZenML deployment may lead to unexpected behavior!
@@ -12470,11 +12179,6 @@ File: docs/book/component-guide/orchestrators/lightning.md
 description: Orchestrating your pipelines to run on Lightning AI.
 ---
-{% hint style="warning" %}
-This is an older version of the ZenML documentation. To read and view the latest version please [visit this up-to-date URL](https://docs.zenml.io).
-{% endhint %}
 # Lightning AI Orchestrator
@@ -12674,11 +12378,6 @@ File: docs/book/component-guide/orchestrators/local-docker.md
 description: Orchestrating your pipelines to run in Docker.
 ---
-{% hint style="warning" %}
-This is an older version of the ZenML documentation. To read and view the latest version please [visit this up-to-date URL](https://docs.zenml.io).
-{% endhint %}
 # Local Docker Orchestrator
 The local Docker orchestrator is an [orchestrator](./orchestrators.md) flavor that comes built-in with ZenML and runs your pipelines locally using Docker.
@@ -12756,11 +12455,6 @@ File: docs/book/component-guide/orchestrators/local.md
 description: Orchestrating your pipelines to run locally.
 ---
-{% hint style="warning" %}
-This is an older version of the ZenML documentation. To read and view the latest version please [visit this up-to-date URL](https://docs.zenml.io).
-{% endhint %}
 # Local Orchestrator
 The local orchestrator is an [orchestrator](./orchestrators.md) flavor that comes built-in with ZenML and runs your pipelines locally.
@@ -12894,11 +12588,6 @@ File: docs/book/component-guide/orchestrators/sagemaker.md
 description: Orchestrating your pipelines to run on Amazon Sagemaker.
 ---
-{% hint style="warning" %}
-This is an older version of the ZenML documentation. To read and view the latest version please [visit this up-to-date URL](https://docs.zenml.io).
-{% endhint %}
 # AWS Sagemaker Orchestrator
 [Sagemaker Pipelines](https://aws.amazon.com/sagemaker/pipelines) is a serverless ML workflow tool running on AWS. It is an easy way to quickly run your code in a production-ready, repeatable cloud orchestrator that requires minimal setup without provisioning and paying for standby compute.
@@ -13064,7 +12753,7 @@ Additional configuration for the Sagemaker orchestrator can be passed via `Sagem
 * `sagemaker_session`
 * `entrypoint`
 * `base_job_name`
-* `env`
 For example, settings can be provided and applied in the following way:
@@ -13077,6 +12766,7 @@ from zenml.integrations.aws.flavors.sagemaker_orchestrator_flavor import (
 sagemaker_orchestrator_settings = SagemakerOrchestratorSettings(
     instance_type="ml.m5.large",
     volume_size_in_gb=30,
 )
@@ -13447,11 +13137,6 @@ File: docs/book/component-guide/orchestrators/skypilot-vm.md
 description: Orchestrating your pipelines to run on VMs using SkyPilot.
 ---
-{% hint style="warning" %}
-This is an older version of the ZenML documentation. To read and view the latest version please [visit this up-to-date URL](https://docs.zenml.io).
-{% endhint %}
 # Skypilot VM Orchestrator
 The SkyPilot VM Orchestrator is an integration provided by ZenML that allows you to provision and manage virtual machines (VMs) on any cloud provider supported by the [SkyPilot framework](https://skypilot.readthedocs.io/en/latest/index.html). This integration is designed to simplify the process of running machine learning workloads on the cloud, offering cost savings, high GPU availability, and managed execution, We recommend using the SkyPilot VM Orchestrator if you need access to GPUs for your workloads, but don't want to deal with the complexities of managing cloud infrastructure or expensive managed solutions.
@@ -13974,11 +13659,6 @@ File: docs/book/component-guide/orchestrators/tekton.md
 description: Orchestrating your pipelines to run on Tekton.
 ---
-{% hint style="warning" %}
-This is an older version of the ZenML documentation. To read and view the latest version please [visit this up-to-date URL](https://docs.zenml.io).
-{% endhint %}
 # Tekton Orchestrator
 [Tekton](https://tekton.dev/) is a powerful and flexible open-source framework for creating CI/CD systems, allowing developers to build, test, and deploy across cloud providers and on-premise systems.
@@ -14219,11 +13899,6 @@ File: docs/book/component-guide/orchestrators/vertex.md
 description: Orchestrating your pipelines to run on Vertex AI.
 ---
-{% hint style="warning" %}
-This is an older version of the ZenML documentation. To read and view the latest version please [visit this up-to-date URL](https://docs.zenml.io).
-{% endhint %}
 # Google Cloud VertexAI Orchestrator
 [Vertex AI Pipelines](https://cloud.google.com/vertex-ai/docs/pipelines/introduction) is a serverless ML workflow tool running on the Google Cloud Platform. It is an easy way to quickly run your code in a production-ready, repeatable cloud orchestrator that requires minimal setup without provisioning and paying for standby compute.
@@ -14543,11 +14218,6 @@ File: docs/book/component-guide/step-operators/azureml.md
 description: Executing individual steps in AzureML.
 ---
-{% hint style="warning" %}
-This is an older version of the ZenML documentation. To read and view the latest version please [visit this up-to-date URL](https://docs.zenml.io).
-{% endhint %}
 # AzureML
 [AzureML](https://azure.microsoft.com/en-us/products/machine-learning/) offers specialized compute instances to run your training jobs and has a comprehensive UI to track and manage your models and logs. ZenML's AzureML step operator allows you to submit individual steps to be run on AzureML compute instances.
@@ -14709,11 +14379,6 @@ File: docs/book/component-guide/step-operators/custom.md
 description: Learning how to develop a custom step operator.
 ---
-{% hint style="warning" %}
-This is an older version of the ZenML documentation. To read and view the latest version please [visit this up-to-date URL](https://docs.zenml.io).
-{% endhint %}
 # Develop a Custom Step Operator
 {% hint style="info" %}
@@ -14843,11 +14508,6 @@ File: docs/book/component-guide/step-operators/kubernetes.md
 description: Executing individual steps in Kubernetes Pods.
 ---
-{% hint style="warning" %}
-This is an older version of the ZenML documentation. To read and view the latest version please [visit this up-to-date URL](https://docs.zenml.io).
-{% endhint %}
 # Kubernetes Step Operator
 ZenML's Kubernetes step operator allows you to submit individual steps to be run on Kubernetes pods.
@@ -15082,11 +14742,6 @@ File: docs/book/component-guide/step-operators/modal.md
 description: Executing individual steps in Modal.
 ---
-{% hint style="warning" %}
-This is an older version of the ZenML documentation. To read and view the latest version please [visit this up-to-date URL](https://docs.zenml.io).
-{% endhint %}
 # Modal Step Operator
 [Modal](https://modal.com) is a platform for running cloud infrastructure. It offers specialized compute instances to run your code and has a fast execution time, especially around building Docker images and provisioning hardware. ZenML's Modal step operator allows you to submit individual steps to be run on Modal compute instances.
@@ -15204,11 +14859,6 @@ File: docs/book/component-guide/step-operators/sagemaker.md
 description: Executing individual steps in SageMaker.
 ---
-{% hint style="warning" %}
-This is an older version of the ZenML documentation. To read and view the latest version please [visit this up-to-date URL](https://docs.zenml.io).
-{% endhint %}
 # Amazon SageMaker
 [SageMaker](https://aws.amazon.com/sagemaker/) offers specialized compute instances to run your training jobs and has a comprehensive UI to track and manage your models and logs. ZenML's SageMaker step operator allows you to submit individual steps to be run on Sagemaker compute instances.
@@ -15574,11 +15224,6 @@ roleRef:
   name: edit
   apiGroup: rbac.authorization.k8s.io
 ---
-{% hint style="warning" %}
-This is an older version of the ZenML documentation. To read and view the latest version please [visit this up-to-date URL](https://docs.zenml.io).
-{% endhint %}
 ```
 And then execute the following command to create the resources:
@@ -15747,11 +15392,6 @@ File: docs/book/component-guide/step-operators/vertex.md
 description: Executing individual steps in Vertex AI.
 ---
-{% hint style="warning" %}
-This is an older version of the ZenML documentation. To read and view the latest version please [visit this up-to-date URL](https://docs.zenml.io).
-{% endhint %}
 # Google Cloud VertexAI
 [Vertex AI](https://cloud.google.com/vertex-ai) offers specialized compute instances to run your training jobs and has a comprehensive UI to track and manage your models and logs. ZenML's Vertex AI step operator allows you to submit individual steps to be run on Vertex AI compute instances.
@@ -15942,11 +15582,6 @@ File: docs/book/component-guide/component-guide.md
 description: Overview of categories of MLOps components.
 ---
-{% hint style="warning" %}
-This is an older version of the ZenML documentation. To read and view the latest version please [visit this up-to-date URL](https://docs.zenml.io).
-{% endhint %}
 # 📜 Overview
 If you are new to the world of MLOps, it is often daunting to be immediately faced with a sea of tools that seemingly all promise and do the same things. It is useful in this case to try to categorize tools in various groups in order to understand their value in your toolchain in a more precise manner.
@@ -15985,11 +15620,6 @@ File: docs/book/component-guide/integration-overview.md
 description: Overview of third-party ZenML integrations.
 ---
-{% hint style="warning" %}
-This is an older version of the ZenML documentation. To read and view the latest version please [visit this up-to-date URL](https://docs.zenml.io).
-{% endhint %}
 # Integration overview
 Categorizing the MLOps stack is a good way to write abstractions for an MLOps pipeline and standardize your processes. But ZenML goes further and also provides concrete implementations of these categories by **integrating** with various tools for each category. Once code is organized into a ZenML pipeline, you can supercharge your ML workflows with the best-in-class solutions from various MLOps areas.

 icon: message-exclamation
 ---
 # Alerters
 **Alerters** allow you to send messages to chat services (like Slack, Discord, Mattermost, etc.) from within your
 description: Learning how to develop a custom alerter.
 ---
 # Develop a Custom Alerter
 {% hint style="info" %}
 description: Sending automated alerts to a Discord channel.
 ---
 # Discord Alerter
 The `DiscordAlerter` enables you to send messages to a dedicated Discord channel
 description: Sending automated alerts to a Slack channel.
 ---
 # Slack Alerter
 The `SlackAlerter` enables you to send messages or ask questions within a
 description: Annotating data using Argilla.
 ---
 # Argilla
 [Argilla](https://github.com/argilla-io/argilla) is a collaboration tool for AI engineers and domain experts who need to build high-quality datasets for their projects. It enables users to build robust language models through faster data curation using both human and machine feedback, providing support for each step in the MLOps cycle, from data labeling to model monitoring.
 description: Learning how to develop a custom annotator.
 ---
 # Develop a Custom Annotator
 {% hint style="info" %}
 description: Annotating data using Label Studio.
 ---
 # Label Studio
 Label Studio is one of the leading open-source annotation platforms available to data scientists and ML practitioners.
 description: Annotating data using Pigeon.
 ---
 # Pigeon
 Pigeon is a lightweight, open-source annotation tool designed for quick and easy labeling of data directly within Jupyter notebooks. It provides a simple and intuitive interface for annotating various types of data, including:
 description: Annotating data using Prodigy.
 ---
 # Prodigy
 [Prodigy](https://prodi.gy/) is a modern annotation tool for creating training
 icon: folder-closed
 ---
 # Artifact Stores
 The Artifact Store is a central component in any MLOps stack. As the name suggests, it acts as a data persistence layer where artifacts (e.g. datasets, models) ingested or generated by the machine learning pipelines are stored.
 description: Storing artifacts using Azure Blob Storage
 ---
 # Azure Blob Storage
 The Azure Artifact Store is an [Artifact Store](./artifact-stores.md) flavor provided with the Azure ZenML integration that uses [the Azure Blob Storage managed object storage service](https://azure.microsoft.com/en-us/services/storage/blobs/) to store ZenML artifacts in an Azure Blob Storage container.
 description: Learning how to develop a custom artifact store.
 ---
 # Develop a custom artifact store
 {% hint style="info" %}
 description: Storing artifacts using GCP Cloud Storage.
 ---
 # Google Cloud Storage (GCS)
 The GCS Artifact Store is an [Artifact Store](./artifact-stores.md) flavor provided with the GCP ZenML integration that uses [the Google Cloud Storage managed object storage service](https://cloud.google.com/storage/docs/introduction) to store ZenML artifacts in a GCP Cloud Storage bucket.
 description: Storing artifacts on your local filesystem.
 ---
 # Local Artifact Store
 The local Artifact Store is a built-in ZenML [Artifact Store](./artifact-stores.md) flavor that uses a folder on your local filesystem to store artifacts.
 description: Storing artifacts in an AWS S3 bucket.
 ---
 # Amazon Simple Cloud Storage (S3)
 The S3 Artifact Store is an [Artifact Store](./artifact-stores.md) flavor provided with the S3 ZenML integration that uses [the AWS S3 managed object storage service](https://aws.amazon.com/s3/) or one of the self-hosted S3 alternatives, such as [MinIO](https://min.io/) or [Ceph RGW](https://ceph.io/en/discover/technology/#object), to store artifacts in an S3 compatible object storage backend.
 description: Storing container images in Amazon ECR.
 ---
 # Amazon Elastic Container Registry (ECR)
 The AWS container registry is a [container registry](./container-registries.md) flavor provided with the ZenML `aws` integration and uses [Amazon ECR](https://aws.amazon.com/ecr/) to store container images.
 description: Storing container images in Azure.
 ---
 # Azure Container Registry
 The Azure container registry is a [container registry](./container-registries.md) flavor that comes built-in with ZenML and uses the [Azure Container Registry](https://azure.microsoft.com/en-us/services/container-registry/) to store container images.
 description: Learning how to develop a custom container registry.
 ---
 # Develop a custom container registry
 {% hint style="info" %}
 description: Storing container images locally.
 ---
 # Default Container Registry
 The Default container registry is a [container registry](./container-registries.md) flavor that comes built-in with ZenML and allows container registry URIs of any format.
 description: Storing container images in DockerHub.
 ---
 # DockerHub
 The DockerHub container registry is a [container registry](./container-registries.md) flavor that comes built-in with ZenML and uses [DockerHub](https://hub.docker.com/) to store container images.
 description: Storing container images in GCP.
 ---
 # Google Cloud Container Registry
 The GCP container registry is a [container registry](./container-registries.md) flavor that comes built-in with ZenML and uses the [Google Artifact Registry](https://cloud.google.com/artifact-registry).
 {% endtab %}
 {% tab title="GCP Service Connector (recommended)" %}
+To set up the GCP Container Registry to authenticate to GCP and access a Google Artifact Registry, it is recommended to leverage the many features provided by [the GCP Service Connector](../../how-to/infrastructure-deployment/auth-management/gcp-service-connector.md) such as auto-configuration, local login, best security practices regarding long-lived credentials and reusing the same credentials across multiple stack components.
+If you don't already have a GCP Service Connector configured in your ZenML deployment, you can register one using the interactive CLI command. You have the option to configure a GCP Service Connector that can be used to access a Google Artifact Registry or even more than one type of GCP resource:
 ```sh
 zenml service-connector register --type gcp -i
 description: Storing container images in GitHub.
 ---
 # GitHub Container Registry
 The GitHub container registry is a [container registry](./container-registries.md) flavor that comes built-in with ZenML and uses the [GitHub Container Registry](https://docs.github.com/en/packages/working-with-a-github-packages-registry/working-with-the-container-registry) to store container images.
 description: How to develop a custom data validator
 ---
 # Develop a custom data validator
 {% hint style="info" %}
   suites
 ---
 # Deepchecks
 The Deepchecks [Data Validator](./data-validators.md) flavor provided with the ZenML integration uses [Deepchecks](https://deepchecks.com/) to run data integrity, data drift, model drift and model performance tests on the datasets and models circulated in your ZenML pipelines. The test results can be used to implement automated corrective actions in your pipelines or to render interactive representations for further visual interpretation, evaluation and documentation.
   with Evidently profiling
 ---
 # Evidently
 The Evidently [Data Validator](./data-validators.md) flavor provided with the ZenML integration uses [Evidently](https://evidentlyai.com/) to perform data quality, data drift, model drift and model performance analyzes, to generate reports and run checks. The reports and check results can be used to implement automated corrective actions in your pipelines or to render interactive representations for further visual interpretation, evaluation and documentation.
   document the results
 ---
 # Great Expectations
 The Great Expectations [Data Validator](./data-validators.md) flavor provided with the ZenML integration uses [Great Expectations](https://greatexpectations.io/) to run data profiling and data quality tests on the data circulated through your pipelines. The test results can be used to implement automated corrective actions in your pipelines. They are also automatically rendered into documentation for further visual interpretation and evaluation.
   data with whylogs/WhyLabs profiling.
 ---
 # Whylogs
 The whylogs/WhyLabs [Data Validator](./data-validators.md) flavor provided with the ZenML integration uses [whylogs](https://whylabs.ai/whylogs) and [WhyLabs](https://whylabs.ai) to generate and track data profiles, highly accurate descriptive representations of your data. The profiles can be used to implement automated corrective actions in your pipelines, or to render interactive representations for further visual interpretation, evaluation and documentation.
 description: Logging and visualizing experiments with Comet.
 ---
 # Comet
 The Comet Experiment Tracker is an [Experiment Tracker](./experiment-trackers.md) flavor provided with the Comet ZenML integration that uses [the Comet experiment tracking platform](https://www.comet.com/site/products/ml-experiment-tracking/) to log and visualize information from your pipeline steps (e.g., models, parameters, metrics).
 description: Learning how to develop a custom experiment tracker.
 ---
 # Develop a custom experiment tracker
 {% hint style="info" %}
 icon: clipboard
 ---
 # Experiment Trackers
 Experiment trackers let you track your ML experiments by logging extended information about your models, datasets,
 description: Logging and visualizing experiments with MLflow.
 ---
 # MLflow
 The MLflow Experiment Tracker is an [Experiment Tracker](./experiment-trackers.md) flavor provided with the MLflow ZenML integration that uses [the MLflow tracking service](https://mlflow.org/docs/latest/tracking.html) to log and visualize information from your pipeline steps (e.g. models, parameters, metrics).
 description: Logging and visualizing experiments with neptune.ai
 ---
 # Neptune
 The Neptune Experiment Tracker is an [Experiment Tracker](./experiment-trackers.md) flavor provided with the Neptune-ZenML integration that uses [neptune.ai](https://neptune.ai/product/experiment-tracking) to log and visualize information from your pipeline steps (e.g. models, parameters, metrics).
 description: Logging and visualizing experiments with Vertex AI Experiment Tracker.
 ---
 # Vertex AI Experiment Tracker
 The Vertex AI Experiment Tracker is an [Experiment Tracker](./experiment-trackers.md) flavor provided with the Vertex AI ZenML integration. It uses the [Vertex AI tracking service](https://cloud.google.com/vertex-ai/docs/experiments/intro-vertex-ai-experiments) to log and visualize information from your pipeline steps (e.g., models, parameters, metrics).
 description: Logging and visualizing experiments with Weights & Biases.
 ---
 # Weights & Biases
 The Weights & Biases Experiment Tracker is an [Experiment Tracker](./experiment-trackers.md) flavor provided with the Weights & Biases ZenML integration that uses [the Weights & Biases experiment tracking platform](https://wandb.ai/site/experiment-tracking) to log and visualize information from your pipeline steps (e.g. models, parameters, metrics).
 description: Learning how to develop a custom feature store.
 ---
 # Develop a Custom Feature Store
 {% hint style="info" %}
 description: Managing data in Feast feature stores.
 ---
 # Feast
 Feast (Feature Store) is an operational data system for managing and serving machine learning features to models in production. Feast is able to serve feature data to models from a low-latency online store (for real-time prediction) or from an offline store (for scale-out batch scoring or model training).
 description: Building container images with AWS CodeBuild
 ---
 # AWS Image Builder
 The AWS image builder is an [image builder](./image-builders.md) flavor provided by the ZenML `aws` integration that uses [AWS CodeBuild](https://aws.amazon.com/codebuild) to build container images.
 description: Learning how to develop a custom image builder.
 ---
 # Develop a Custom Image Builder
 {% hint style="info" %}
 description: Building container images with Google Cloud Build
 ---
 # Google Cloud Image Builder
 The Google Cloud image builder is an [image builder](./image-builders.md) flavor provided by the ZenML `gcp` integration that uses [Google Cloud Build](https://cloud.google.com/build) to build container images.
 description: Building container images with Kaniko.
 ---
 # Kaniko Image Builder
 The Kaniko image builder is an [image builder](./image-builders.md) flavor provided by the ZenML `kaniko` integration that uses [Kaniko](https://github.com/GoogleContainerTools/kaniko) to build container images.
 * `--cache`: Set to `false` to disable caching. Defaults to `true`.
 * `--cache-dir`: Set the directory where to store cached layers. Defaults to `/cache`.
+* `--cache-repo`: Set the repository where to store cached layers.
 * `--cache-ttl`: Set the cache expiration time. Defaults to `24h`.
 * `--cleanup`: Set to `false` to disable cleanup of the working directory. Defaults to `true`.
 * `--compressed-caching`: Set to `false` to disable compressed caching. Defaults to `true`.
 description: Building container images locally.
 ---
 # Local Image Builder
 The local image builder is an [image builder](./image-builders.md) flavor that comes built-in with ZenML and uses the local Docker installation on your client machine to build container images.
 description: Deploying your models locally with BentoML.
 ---
 # BentoML
 BentoML is an open-source framework for machine learning model serving. it can be used to deploy models locally, in a cloud environment, or in a Kubernetes environment.
 ### Create a BentoML Service
+The first step to being able to deploy your models and use BentoML is to create a [bento service](https://docs.bentoml.com/en/latest/guides/services.html) which is the main logic that defines how your model will be served.
 The following example shows how to create a basic bento service that will be used to serve a torch model. Learn more about how to specify the inputs and outputs for the APIs and how to use validators in the [Input and output types BentoML docs](https://docs.bentoml.com/en/latest/guides/iotypes.html)
 description: Learning how to develop a custom model deployer.
 ---
 # Develop a Custom Model Deployer
 {% hint style="info" %}
   Deploying models to Databricks Inference Endpoints with Databricks
 ---
 # Databricks
   :hugging_face:.
 ---
 # Hugging Face
 Hugging Face Inference Endpoints provides a secure production solution to easily deploy any `transformers`, `sentence-transformers`, and `diffusers` models on a dedicated and autoscaling infrastructure managed by Hugging Face. An Inference Endpoint is built from a model from the [Hub](https://huggingface.co/models).
 description: Deploying your models locally with MLflow.
 ---
 # MLflow
 The MLflow Model Deployer is one of the available flavors of the [Model Deployer](./model-deployers.md) stack component. Provided with the MLflow integration it can be used to deploy and manage [MLflow models](https://www.mlflow.org/docs/latest/python\_api/mlflow.deployments.html) on a local running MLflow server.
    ```
 * Lifecycle Management: Provides mechanisms for comprehensive lifecycle management of model servers, including the ability to start, stop, and delete model servers, as well as to update existing servers with new model versions, thereby optimizing resource utilization and facilitating continuous delivery of model updates. Some core methods that can be used to interact with the remote model server include:
+  - `deploy_model` - Deploys a model to the serving environment and returns a Service object that represents the deployed model server.
+  - `find_model_server` - Finds and returns a list of Service objects that
+    represent model servers that have been deployed to the serving environment,
+    the `services` are stored in the DB and can be used as a reference to know what and where the model is deployed.
+  - `stop_model_server` - Stops a model server that is currently running in the serving environment.
+  - `start_model_server` - Starts a model server that has been stopped in the serving environment.
+  - `delete_model_server` - Deletes a model server from the serving environment and from the DB.
 {% hint style="info" %}
 ZenML uses the Service object to represent a model server that has been deployed to a serving environment. The Service object is saved in the DB and can be used as a reference to know what and where the model is deployed. The Service object consists of 2 main attributes, the `config` and the `status`. The `config` attribute holds all the deployment configuration attributes required to create a new deployment, while the `status` attribute holds the operational status of the deployment, such as the last error message, the prediction URL, and the deployment status.
 description: Deploying models to Kubernetes with Seldon Core.
 ---
 # Seldon
 [Seldon Core](https://github.com/SeldonIO/seldon-core) is a production grade source-available model serving platform. It packs a wide range of features built around deploying models to REST/GRPC microservices that include monitoring and logging, model explainers, outlier detectors and various continuous deployment strategies such as A/B testing, canary deployments and more.
 description: Deploying your LLM locally with vLLM.
 ---
 # vLLM
 [vLLM](https://docs.vllm.ai/en/latest/) is a fast and easy-to-use library for LLM inference and serving.
 description: Learning how to develop a custom model registry.
 ---
 # Develop a Custom Model Registry
 {% hint style="info" %}
 description: Managing MLFlow logged models and artifacts
 ---
 # MLflow Model Registry
 [MLflow](https://www.mlflow.org/docs/latest/tracking.html) is a popular tool that helps you track experiments, manage models and even deploy them to different environments. ZenML already provides a [MLflow Experiment Tracker](../experiment-trackers/mlflow.md) that you can use to track your experiments, and an [MLflow Model Deployer](../model-deployers/mlflow.md) that you can use to deploy your models locally.
 description: Orchestrating your pipelines to run on Airflow.
 ---
 # Airflow Orchestrator
 ZenML pipelines can be executed natively as [Airflow](https://airflow.apache.org/)
 description: Orchestrating your pipelines to run on AzureML.
 ---
 # AzureML Orchestrator
 [AzureML](https://azure.microsoft.com/en-us/products/machine-learning) is a
 description: Learning how to develop a custom orchestrator.
 ---
 # Develop a custom orchestrator
 {% hint style="info" %}
 description: Orchestrating your pipelines to run on Databricks.
 ---
 # Databricks Orchestrator
 [Databricks](https://www.databricks.com/) is a unified data analytics platform that combines the best of data warehouses and data lakes to offer an integrated solution for big data processing and machine learning. It provides a collaborative environment for data scientists, data engineers, and business analysts to work together on data projects. Databricks offers optimized performance and scalability for big data workloads.
 description: Orchestrating your pipelines to run on HyperAI.ai instances.
 ---
 # HyperAI Orchestrator
 [HyperAI](https://www.hyperai.ai) is a cutting-edge cloud compute platform designed to make AI accessible for everyone. The HyperAI orchestrator is an [orchestrator](./orchestrators.md) flavor that allows you to easily deploy your pipelines on HyperAI instances.
 description: Orchestrating your pipelines to run on Kubeflow.
 ---
 # Kubeflow Orchestrator
 The Kubeflow orchestrator is an [orchestrator](./orchestrators.md) flavor provided by the ZenML `kubeflow` integration that uses [Kubeflow Pipelines](https://www.kubeflow.org/docs/components/pipelines/overview/) to run your pipelines.
 description: Orchestrating your pipelines to run on Kubernetes clusters.
 ---
 # Kubernetes Orchestrator
 Using the ZenML `kubernetes` integration, you can orchestrate and scale your ML pipelines on a [Kubernetes](https://kubernetes.io/) cluster without writing a single line of Kubernetes code.
 Overall, the Kubernetes orchestrator is quite similar to the Kubeflow orchestrator in that it runs each pipeline step in a separate Kubernetes pod. However, the orchestration of the different pods is not done by Kubeflow but by a separate master pod that orchestrates the step execution via topological sort.
+Compared to Kubeflow, this means that the Kubernetes-native orchestrator is faster and much simpler since you do not need to install and maintain Kubeflow on your cluster. The Kubernetes-native orchestrator is an ideal choice for teams in need of distributed orchestration that do not want to go with a fully-managed offering.
 {% hint style="warning" %}
 This component is only meant to be used within the context of a [remote ZenML deployment scenario](../../getting-started/deploying-zenml/README.md). Usage with a local ZenML deployment may lead to unexpected behavior!
 description: Orchestrating your pipelines to run on Lightning AI.
 ---
 # Lightning AI Orchestrator
 description: Orchestrating your pipelines to run in Docker.
 ---
 # Local Docker Orchestrator
 The local Docker orchestrator is an [orchestrator](./orchestrators.md) flavor that comes built-in with ZenML and runs your pipelines locally using Docker.
 description: Orchestrating your pipelines to run locally.
 ---
 # Local Orchestrator
 The local orchestrator is an [orchestrator](./orchestrators.md) flavor that comes built-in with ZenML and runs your pipelines locally.
 description: Orchestrating your pipelines to run on Amazon Sagemaker.
 ---
 # AWS Sagemaker Orchestrator
 [Sagemaker Pipelines](https://aws.amazon.com/sagemaker/pipelines) is a serverless ML workflow tool running on AWS. It is an easy way to quickly run your code in a production-ready, repeatable cloud orchestrator that requires minimal setup without provisioning and paying for standby compute.
 * `sagemaker_session`
 * `entrypoint`
 * `base_job_name`
+* `environment`
 For example, settings can be provided and applied in the following way:
 sagemaker_orchestrator_settings = SagemakerOrchestratorSettings(
     instance_type="ml.m5.large",
     volume_size_in_gb=30,
+    environment={"MY_ENV_VAR": "my_value"}
 )
 description: Orchestrating your pipelines to run on VMs using SkyPilot.
 ---
 # Skypilot VM Orchestrator
 The SkyPilot VM Orchestrator is an integration provided by ZenML that allows you to provision and manage virtual machines (VMs) on any cloud provider supported by the [SkyPilot framework](https://skypilot.readthedocs.io/en/latest/index.html). This integration is designed to simplify the process of running machine learning workloads on the cloud, offering cost savings, high GPU availability, and managed execution, We recommend using the SkyPilot VM Orchestrator if you need access to GPUs for your workloads, but don't want to deal with the complexities of managing cloud infrastructure or expensive managed solutions.
 description: Orchestrating your pipelines to run on Tekton.
 ---
 # Tekton Orchestrator
 [Tekton](https://tekton.dev/) is a powerful and flexible open-source framework for creating CI/CD systems, allowing developers to build, test, and deploy across cloud providers and on-premise systems.
 description: Orchestrating your pipelines to run on Vertex AI.
 ---
 # Google Cloud VertexAI Orchestrator
 [Vertex AI Pipelines](https://cloud.google.com/vertex-ai/docs/pipelines/introduction) is a serverless ML workflow tool running on the Google Cloud Platform. It is an easy way to quickly run your code in a production-ready, repeatable cloud orchestrator that requires minimal setup without provisioning and paying for standby compute.
 description: Executing individual steps in AzureML.
 ---
 # AzureML
 [AzureML](https://azure.microsoft.com/en-us/products/machine-learning/) offers specialized compute instances to run your training jobs and has a comprehensive UI to track and manage your models and logs. ZenML's AzureML step operator allows you to submit individual steps to be run on AzureML compute instances.
 description: Learning how to develop a custom step operator.
 ---
 # Develop a Custom Step Operator
 {% hint style="info" %}
 description: Executing individual steps in Kubernetes Pods.
 ---
 # Kubernetes Step Operator
 ZenML's Kubernetes step operator allows you to submit individual steps to be run on Kubernetes pods.
 description: Executing individual steps in Modal.
 ---
 # Modal Step Operator
 [Modal](https://modal.com) is a platform for running cloud infrastructure. It offers specialized compute instances to run your code and has a fast execution time, especially around building Docker images and provisioning hardware. ZenML's Modal step operator allows you to submit individual steps to be run on Modal compute instances.
 description: Executing individual steps in SageMaker.
 ---
 # Amazon SageMaker
 [SageMaker](https://aws.amazon.com/sagemaker/) offers specialized compute instances to run your training jobs and has a comprehensive UI to track and manage your models and logs. ZenML's SageMaker step operator allows you to submit individual steps to be run on Sagemaker compute instances.
   name: edit
   apiGroup: rbac.authorization.k8s.io
 ---
 ```
 And then execute the following command to create the resources:
 description: Executing individual steps in Vertex AI.
 ---
 # Google Cloud VertexAI
 [Vertex AI](https://cloud.google.com/vertex-ai) offers specialized compute instances to run your training jobs and has a comprehensive UI to track and manage your models and logs. ZenML's Vertex AI step operator allows you to submit individual steps to be run on Vertex AI compute instances.
 description: Overview of categories of MLOps components.
 ---
 # 📜 Overview
 If you are new to the world of MLOps, it is often daunting to be immediately faced with a sea of tools that seemingly all promise and do the same things. It is useful in this case to try to categorize tools in various groups in order to understand their value in your toolchain in a more precise manner.
 description: Overview of third-party ZenML integrations.
 ---
 # Integration overview
 Categorizing the MLOps stack is a good way to write abstractions for an MLOps pipeline and standardize your processes. But ZenML goes further and also provides concrete implementations of these categories by **integrating** with various tools for each category. Once code is organized into a ZenML pipeline, you can supercharge your ML workflows with the best-in-class solutions from various MLOps areas.