Fixing links after the docs update (#76)

* fixing links

* fixing even more links due to previous changes"

CONTRIBUTING.md

@@ -35,8 +35,6 @@ general guidelines that cover both:
   at [[email protected]](mailto:[email protected]), monitored by
   our security team.
 - Search for existing Issues and PRs before creating your own.
-- Search on the [public Crowd Dev](https://open.crowd.dev/zenml) page to see 
-if the issue has been addressed before.
 - We work hard to make sure issues are handled on time, but it could take a
   while to investigate the root cause depending on the impact.
 

customer-churn/README.md

@@ -42,7 +42,7 @@ We showcase two solutions to this problem:
 
 ## Deploy pipelines to production using orchestrator Pipelines
 
-We will be using ZenML's [Kubeflow](https://github.com/zenml-io/zenml/tree/main/examples/kubeflow_pipelines_orchestration) integration to deploy pipelines to production using Kubeflow Pipelines on the cloud.
+We will be using ZenML's [Kubeflow](https://docs.zenml.io/stacks-and-components/component-guide/orchestrators/kubeflow) integration to deploy pipelines to production using Kubeflow Pipelines on the cloud.
 
 Our training pipeline `run_kubeflow_pipeline.py` will be built using the following steps:
 
@@ -93,7 +93,7 @@ a backend
 
 ### Setup Infrastructure with ZenML Stack recipes:
 
-With [ZenML Stack Recipes](https://docs.zenml.io/platform-guide/set-up-your-mlops-platform/deploy-and-set-up-a-cloud-stack/deploy-a-stack-using-stack-recipes), you can now provision all the infrastructure you need to run your ZenML pipelines with just a few simple commands.
+With [ZenML Stack Recipes](https://github.com/zenml-io/mlops-stacks), you can now provision all the infrastructure you need to run your ZenML pipelines with just a few simple commands.
 
 The flow to get started for this example can be the following:
 
@@ -154,11 +154,11 @@ Seldon Core. The following diagram shows the flow of the whole pipeline:
 
 ## Continuous model deployment with Seldon Core
 
-While building the real-world workflow for predicting whether a customer will churn or not, you might not want to train the model once and deploy it to production. Instead, you might want to train the model and deploy it to production when something gets triggered. This is where one of our recent integrations is valuable: [Seldon Core](https://github.com/zenml-io/zenml/tree/main/examples/seldon_deployment).
+While building the real-world workflow for predicting whether a customer will churn or not, you might not want to train the model once and deploy it to production. Instead, you might want to train the model and deploy it to production when something gets triggered. This is where one of our recent integrations is valuable: [Seldon Core](https://docs.zenml.io/stacks-and-components/component-guide/model-deployers/seldon).
 
 [Seldon Core](https://github.com/SeldonIO/seldon-core) is a production-grade open-source model serving platform. It packs a wide range of features built around deploying models to REST/GRPC microservices, including monitoring and logging, model explainers, outlier detectors, and various continuous deployment strategies such as A/B testing and canary deployments, and more.
 
-In this project, we build a continuous deployment pipeline that trains a model and then serves it with Seldon Core as the industry-ready model deployment tool of choice. If you are interested in learning more about Seldon Core, you can check out the [ZenML example](https://github.com/zenml-io/zenml/tree/main/examples/seldon_deployment). The following diagram shows the flow of the whole pipeline:
+In this project, we build a continuous deployment pipeline that trains a model and then serves it with Seldon Core as the industry-ready model deployment tool of choice. If you are interested in learning more about Seldon Core, you can check out our [docs](https://docs.zenml.io/stacks-and-components/component-guide/model-deployers/seldon). The following diagram shows the flow of the whole pipeline:
 ![seldondeployment](_assets/seldoncondeploy.gif)
 
 

customer-satisfaction/README.md

@@ -56,7 +56,7 @@ Instead, we are building an end-to-end pipeline for continuously predicting and
 
 This pipeline can be deployed to the cloud, scale up according to our needs, and ensure that we track the parameters and data that flow through every pipeline that runs. It includes raw data input, features, results, the machine learning model and model parameters, and prediction outputs. ZenML helps us to build such a pipeline in a simple, yet powerful, way.
 
-In this Project, we give special consideration to the [MLflow integration](https://github.com/zenml-io/zenml/tree/main/examples) of ZenML. In particular, we utilize MLflow tracking to track our metrics and parameters, and MLflow deployment to deploy our model. We also use [Streamlit](https://streamlit.io/) to showcase how this model will be used in a real-world setting.
+In this Project, we give special consideration to the [MLflow integration](https://docs.zenml.io/stacks-and-components/component-guide/model-deployers/mlflow) of ZenML. In particular, we utilize MLflow tracking to track our metrics and parameters, and MLflow deployment to deploy our model. We also use [Streamlit](https://streamlit.io/) to showcase how this model will be used in a real-world setting.
 
 ### Training Pipeline
 
@@ -92,7 +92,7 @@ service = load_last_service_from_step(
 service.predict(...)  # Predict on incoming data from the application
 ```
 
-While this ZenML Project trains and deploys a model locally, other ZenML integrations such as the [Seldon](https://github.com/zenml-io/zenml/tree/main/examples/seldon_deployment) deployer can also be used in a similar manner to deploy the model in a more production setting (such as on a Kubernetes cluster). We use MLflow here for the convenience of its local deployment.
+While this ZenML Project trains and deploys a model locally, other ZenML integrations such as the [Seldon](https://docs.zenml.io/stacks-and-components/component-guide/model-deployers/seldon) deployer can also be used in a similar manner to deploy the model in a more production setting (such as on a Kubernetes cluster). We use MLflow here for the convenience of its local deployment.
 
 ![training_and_deployment_pipeline](_assets/training_and_deployment_pipeline_updated.png)
 

langchain-llamaindex-slackbot/README.md

@@ -94,13 +94,13 @@ example](https://github.com/zenml-io/zenml/tree/develop/examples/generative_chat
 
 It is much more ideal to run a pipeline such as the 
 `zenml_docs_index_generation` on a regular schedule. In order to achieve that, 
-you have to [deploy ZenML](https://docs.zenml.io/platform-guide/set-up-your-mlops-platform/deploy-zenml) 
+you have to [deploy ZenML](https://docs.zenml.io/user-guide/starter-guide/switch-to-production) 
 and set up a stack that supports 
 [our scheduling
-feature](https://docs.zenml.io/user-guide/advanced-guide/schedule-pipeline-runs). If you
+feature](https://docs.zenml.io/user-guide/advanced-guide/pipelining-features/schedule-pipeline-runs). If you
 wish to deploy the slack bot on GCP Cloud Run as described above, you'll also
 need to be using [a Google Cloud Storage Artifact
-Store](https://docs.zenml.io/user-guide/component-guide/artifact-stores/gcp). Note that
+Store](https://docs.zenml.io/stacks-and-components/component-guide/artifact-stores/gcp). Note that
 certain code artifacts like the `Dockerfile` for this project will also need to
 be adapted for your own particular needs and requirements. Please check [our docs](https://docs.zenml.io/user-guide/starter-guide/follow-best-practices) 
 for more information.

langchain-qa-hub/README.md

@@ -67,4 +67,4 @@ account can submit plugins. To find out how, check out the
 [ZenML Hub Plugin Template](https://github.com/zenml-io/zenml-hub-plugin-template).
 
 If you would like to learn more about the ZenML Hub in general, check out the
-[ZenML Hub Documentation](https://docs.zenml.io/user-guide/advanced-guide/leverage-community-contributed-plugins) or the [ZenML Hub Launch Blog Post](https://blog.zenml.io/zenml-hub-launch).
+[ZenML Hub Documentation](https://docs.zenml.io/user-guide/advanced-guide/environment-management/use-the-hub) or the [ZenML Hub Launch Blog Post](https://blog.zenml.io/zenml-hub-launch).

langchain-qa-hub/langchain-qa-hub.ipynb

@@ -220,7 +220,7 @@
     "[ZenML Hub Plugin Template](https://github.com/zenml-io/zenml-hub-plugin-template).\n",
     "\n",
     "If you would like to learn more about the ZenML Hub in general, check out the\n",
-    "[ZenML Hub Documentation](https://docs.zenml.io/user-guide/advanced-guide/leverage-community-contributed-plugins) or the [ZenML Hub Launch Blog Post](https://blog.zenml.io/zenml-hub-launch)."
+    "[ZenML Hub Documentation](https://docs.zenml.io/user-guide/advanced-guide/environment-management/use-the-hub) or the [ZenML Hub Launch Blog Post](https://blog.zenml.io/zenml-hub-launch)."
    ]
   }
  ],

orbit-user-analysis/README.md

@@ -65,9 +65,9 @@ python run.py
 
 It is much more ideal to run a pipeline such as the 
 `community_analysis_pipeline` on a regular schedule. In order to achieve that, 
-you have to [deploy ZenML](https://docs.zenml.io/platform-guide/set-up-your-mlops-platform/deploy-zenml) 
+you have to [deploy ZenML](https://docs.zenml.io/user-guide/starter-guide/switch-to-production) 
 and set up a stack that supports 
-[our scheduling feature](https://docs.zenml.io/user-guide/advanced-guide/schedule-pipeline-runs). 
+[our scheduling feature](https://docs.zenml.io/user-guide/advanced-guide/pipelining-features/schedule-pipeline-runs). 
 Please check [our docs](https://docs.zenml.io/getting-started/introduction) 
 for more information.
 

sign-language-detection-yolov5/README.md

@@ -19,7 +19,7 @@ In order to build a model that can detect and recognize the American Sign Langua
 
 1. Download the dataset from [Roboflow](https://public.roboflow.com/object-detection/american-sign-language-alphabet)
 2. Augment the training and valdiation sets using [Albumentations](https://albumentations.ai/)
-3. Train the model using a pretrained model from [Yolov5](https://github.com/ultralytics/yolov5) while tracking the hyperparameters and metrics using [MLflow](https://docs.zenml.io/user-guide/component-guide/experiment-trackers/mlflow) within a GPU environment by leveraging [Google's Vertex AI Step Operator](https://docs.zenml.io/user-guide/component-guide/orchestrators/vertex) stack component.
+3. Train the model using a pretrained model from [Yolov5](https://github.com/ultralytics/yolov5) while tracking the hyperparameters and metrics using [MLflow](https://docs.zenml.io/stacks-and-components/component-guide/experiment-trackers/mlflow) within a GPU environment by leveraging [Google's Vertex AI Step Operator](https://docs.zenml.io/stacks-and-components/component-guide/orchestrators/vertex) stack component.
 4. Load the model in a different pipeline that deploys the model using [BentoML](https://www.bentoml.com/) and the provided ZenML integration.
 5. Create an inference pipeline that will use the deployed model to detect and recognize the American Sign Language alphabet in test images from the first pipeline.
 
@@ -32,18 +32,18 @@ installed on your local machine:
 * [Docker](https://www.docker.com/)
 * [GCloud CLI](https://cloud.google.com/sdk/docs/install) (authenticated)
 * [MLFlow Tracking Server](https://mlflow.org/docs/latest/tracking.html#mlflow-tracking-servers) (deployed remotely)
-* [Remote ZenML Server](https://docs.zenml.io/platform-guide/set-up-your-mlops-platform/deploy-zenml): a Remote Deployment of the ZenML HTTP server and database
+* [Remote ZenML Server](https://docs.zenml.io/user-guide/starter-guide/switch-to-production): a Remote Deployment of the ZenML HTTP server and database
 
 ### :rocket: Remote ZenML Server
 
 For advanced use cases where we have a remote orchestrator or step operators such as Vertex AI
 or to share stacks and pipeline information with a team we need to have a separated non-local remote ZenML Server that can be accessible from your
 machine as well as all stack components that may need access to the server.
-[Read more information about the use case here](https://docs.zenml.io/platform-guide/set-up-your-mlops-platform/deploy-zenml)
+[Read more information about the use case here](https://docs.zenml.io/user-guide/starter-guide/switch-to-production)
 
 In order to achieve this there are two different ways to get access to a remote ZenML Server.
 
-1. Deploy and manage the server manually on [your own cloud](https://docs.zenml.io/platform-guide/set-up-your-mlops-platform/deploy-zenml)/
+1. Deploy and manage the server manually on [your own cloud](https://docs.zenml.io/user-guide/starter-guide/switch-to-production)/
 2. Sign up for [ZenML Enterprise](https://zenml.io/pricing) and get access to a hosted
    version of the ZenML Server with no setup required.
 
@@ -270,8 +270,7 @@ The Inference pipeline is made up of the following steps:
 
 # 📜 References
 
-- Documentation on [Step Operators](https://docs.zenml.io/user-guide/component-guide/step-operators)
-- Example of [Step Operators](https://github.com/zenml-io/zenml/tree/main/examples/step_operator_remote_training)
+- Documentation on [Step Operators](https://docs.zenml.io/stacks-and-components/component-guide/step-operators)
 - More on [Step Operators](https://blog.zenml.io/step-operators-training/)
 - Documentation on how to create a GCP [service account](https://cloud.google.com/docs/authentication/getting-started#create-service-account-gcloud)
 - ZenML CLI [documentation](https://apidocs.zenml.io/latest/cli/)

supabase-openai-summary/README.md

@@ -29,8 +29,8 @@ pip install -r src/requirements.txt
 ## Connect to Your Deployed ZenML
 
 In order to run a ZenML pipeline remotely (e.g. on the cloud), we first need to
-[deploy ZenML](https://docs.zenml.io/platform-guide/set-up-your-mlops-platform/deploy-zenml). One of the
-easiest ways to do this is to [deploy ZenML with HuggingFace spaces](https://docs.zenml.io/platform-guide/set-up-your-mlops-platform/deploy-zenml/deploy-using-huggingface-spaces).
+[deploy ZenML](https://docs.zenml.io/user-guide/starter-guide/switch-to-production). One of the
+easiest ways to do this is to [deploy ZenML with HuggingFace spaces](https://docs.zenml.io/deploying-zenml/zenml-self-hosted/deploy-using-huggingface-spaces).
 
 Afterward, establish a connection with your deployed ZenML instance:
 
@@ -67,7 +67,7 @@ You can also modify the preset prompts and system inputs in the [`generate_summa
 
 ## Run the Pipeline on a Remote Stack with Alerter
 
-To run the pipeline on a remote stack with [an artifact store](https://docs.zenml.io/user-guide/component-guide/artifact-stores) and [a Slack alerter](https://docs.zenml.io/user-guide/component-guide/alerters/slack), follow these steps:
+To run the pipeline on a remote stack with [an artifact store](https://docs.zenml.io/stacks-and-components/component-guide/artifact-stores) and [a Slack alerter](https://docs.zenml.io/stacks-and-components/component-guide/alerters/slack), follow these steps:
 
 1. Install the GCP and Slack integrations for ZenML:
 
@@ -97,7 +97,7 @@ Once the stack is registered and set active, the pipeline will run on the remote
 
 ## Running in production: Choose your MLOps stack
 
-ZenML simplifies scaling this pipeline by allowing seamless deployment on production-ready orchestrators like [Airflow](https://docs.zenml.io/user-guide/component-guide/orchestrators/airflow) or [Kubeflow](https://docs.zenml.io/user-guide/component-guide/orchestrators/kubeflow). With [native versioning on cloud storage](https://docs.zenml.io/user-guide/starter-guide/cache-previous-executions) and experiment tracking through ZenML's integration with [MLflow](https://docs.zenml.io/user-guide/component-guide/experiment-trackers/mlflow), you can start locally and effortlessly transition to robust and efficient MLOps pipelines in production, unlocking valuable insights from your enterprise data.
+ZenML simplifies scaling this pipeline by allowing seamless deployment on production-ready orchestrators like [Airflow](https://docs.zenml.io/stacks-and-components/component-guide/orchestrators/airflow) or [Kubeflow](https://docs.zenml.io/stacks-and-components/component-guide/orchestrators/kubeflow). With [native versioning on cloud storage](https://docs.zenml.io/user-guide/starter-guide/cache-previous-executions) and experiment tracking through ZenML's integration with [MLflow](https://docs.zenml.io/stacks-and-components/component-guide/experiment-trackers/mlflow), you can start locally and effortlessly transition to robust and efficient MLOps pipelines in production, unlocking valuable insights from your enterprise data.
 
 ## Example: Automate Pipeline Execution with GitHub Actions
 

time-series-forecast/README.md

@@ -195,7 +195,7 @@ docker push gcr.io/<PROJECT-ID>/busybox
 Note that you may need to run `gcloud auth configure-docker` in order to
 authenticate your local `docker` cli with your GCP container registry and in
 order for the `docker push...` command to work. [See our
-documentation](https://docs.zenml.io/user-guide/component-guide/container-registries/gcp)
+documentation](https://docs.zenml.io/stacks-and-components/component-guide/container-registries/gcp)
 for more information on making this work.
 
 ### 6. [Enable](https://console.cloud.google.com/marketplace/product/google/aiplatform.googleapis.com?q=search&referrer=search&project=cloudguru-test-project) `Vertex AI API`
@@ -276,9 +276,7 @@ python main.py
 
 # 📜 References
 
-Documentation on [Step Operators](https://docs.zenml.io/user-guide/component-guide/step-operators)
-
-Example of [Step Operators](https://github.com/zenml-io/zenml/tree/main/examples/step_operator_remote_training)
+Documentation on [Step Operators](https://docs.zenml.io/stacks-and-components/component-guide/step-operators)
 
 More on [Step Operators](https://blog.zenml.io/step-operators-training/)
 

zen-news-summarization/README.md

@@ -127,16 +127,16 @@ and use the `VertexOrchestrator` to schedule the pipeline.
 
 Before you start building the stack, you need to deploy ZenML on GCP. For more 
 information on how you can achieve do that, please check 
-[the corresponding docs page](https://docs.zenml.io/platform-guide/set-up-your-mlops-platform/deploy-zenml).
+[the corresponding docs page](https://docs.zenml.io/user-guide/starter-guide/switch-to-production).
 
 ## ZenNews Stack
 
 Once the ZenML is deployed, we can start to build up our stack. Our stack will 
 consist of the following components:
 
-- [GCP Container Registry](https://docs.zenml.io/user-guide/component-guide/container-registries/gcp)
-- [GCS Artifact Store](https://docs.zenml.io/user-guide/component-guide/artifact-stores/gcp)
-- [Vertex Orchestrator](https://docs.zenml.io/user-guide/component-guide/orchestrators/vertex)
+- [GCP Container Registry](https://docs.zenml.io/stacks-and-components/component-guide/container-registries/gcp)
+- [GCS Artifact Store](https://docs.zenml.io/stacks-and-components/component-guide/artifact-stores/gcp)
+- [Vertex Orchestrator](https://docs.zenml.io/stacks-and-components/component-guide/orchestrators/vertex)
 - [Discord Alerter (part of the `zennews` package)](src/zennews/alerter/discord_alerter.py)
 
 Let's start by installing the `gcp` integration:
@@ -148,7 +148,7 @@ zenml integration install gcp
 ### Container Registry
 
 The first component is a 
-[GCP container registry](https://docs.zenml.io/user-guide/component-guide/container-registries/gcp). 
+[GCP container registry](https://docs.zenml.io/stacks-and-components/component-guide/container-registries/gcp). 
 Similar to the previous component, you just need to provide a name and the 
 URI to your container registry on GCP.
 
@@ -161,7 +161,7 @@ zenml container-registry register <CONTAINER_REGISTRY_NAME> \
 ### Artifact Store
 
 The next component on the list is a 
-[GCS artifact store](https://docs.zenml.io/user-guide/component-guide/artifact-stores/gcp). 
+[GCS artifact store](https://docs.zenml.io/stacks-and-components/component-guide/artifact-stores/gcp). 
 In order to register it, all you have to do is to provide the path to your GCS
 bucket:
 
@@ -174,7 +174,7 @@ zenml artifact-store register <ARTIFACT_STORE_NAME> \
 ### Orchestrator
 
 Following the artifact store, we will register a
-[Vertex AI orchestrator.](https://docs.zenml.io/user-guide/component-guide/orchestrators/vertex)
+[Vertex AI orchestrator.](https://docs.zenml.io/stacks-and-components/component-guide/orchestrators/vertex)
 
 ```bash
 zenml orchestrator register <ORCHESTRATOR_NAME> \