fixing broken links

huggingface-sagemaker/README.md

@@ -93,7 +93,7 @@ zenml secret create huggingface_creds --username=HUGGINGFACE_USERNAME --token=HU
 <details>
 <summary><h3>Set up your local stack</h3></summary>
 
-To run this project, you need to create a [ZenML Stack](https://docs.zenml.io/user-guide/starter-guide/understand-stacks) with the required components to run the pipelines.
+To run this project, you need to create a [ZenML Stack](https://docs.zenml.io/user-guide/production-guide/understand-stacks) with the required components to run the pipelines.
 
 ```shell
 make install-stack

langchain-llamaindex-slackbot/README.md

@@ -94,15 +94,15 @@ 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/user-guide/starter-guide/switch-to-production) 
+you have to [deploy ZenML](https://docs.zenml.io/user-guide/production-guide/connect-deployed-zenml) 
 and set up a stack that supports 
 [our scheduling
 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/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) 
+be adapted for your own particular needs and requirements. Please check [our docs](https://docs.zenml.io/user-guide/advanced-guide/best-practices) 
 for more information.
 
 ## Slack Bot In Action!

nba-pipeline/README.md

@@ -125,12 +125,12 @@ python run_pipeline.py infer  # Run inference pipeline
 
 ## :rocket: From Local to Cloud Stack
 In ZenML you can choose to run your pipeline on any infrastructure of your choice.
-The configuration of the infrastructure is called a [Stack](https://docs.zenml.io/user-guide/starter-guide/understand-stacks). 
+The configuration of the infrastructure is called a [Stack](https://docs.zenml.io/user-guide/production-guide/understand-stacks). 
 By switching the Stack, you can choose to run your pipeline locally or in the cloud.
 
-In any Stack, there must be at least two basic [Stack Components](https://docs.zenml.io/user-guide/starter-guide/understand-stacks#components-of-a-stack): 
-* [Orchestrator](https://docs.zenml.io/user-guide/starter-guide/understand-stacks#orchestrator) - Coordinates all the steps to run in a pipeline.
-* [Artifact Store](https://docs.zenml.io/user-guide/starter-guide/understand-stacks#artifact-store) - Stores all data that pass through the pipeline. 
+In any Stack, there must be at least two basic [Stack Components](https://docs.zenml.io/user-guide/production-guide/understand-stacks#components-of-a-stack): 
+* [Orchestrator](https://docs.zenml.io/user-guide/production-guide/understand-stacks#orchestrator) - Coordinates all the steps to run in a pipeline.
+* [Artifact Store](https://docs.zenml.io/user-guide/production-guide/understand-stacks#artifact-store) - Stores all data that pass through the pipeline. 
 
 ZenML comes with a default local stack with a local orchestrator and local artifact store.
 ![local](_assets/local_cloud.png)

orbit-user-analysis/README.md

@@ -65,7 +65,7 @@ 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/user-guide/starter-guide/switch-to-production) 
+you have to [deploy ZenML](https://docs.zenml.io/user-guide/production-guide/connect-deployed-zenml) 
 and set up a stack that supports 
 [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) 

sign-language-detection-yolov5/README.md

@@ -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/user-guide/starter-guide/switch-to-production): a Remote Deployment of the ZenML HTTP server and database
+* [Remote ZenML Server](https://docs.zenml.io/user-guide/production-guide/connect-deployed-zenml): 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/user-guide/starter-guide/switch-to-production)
+[Read more information about the use case here](https://docs.zenml.io/user-guide/production-guide/connect-deployed-zenml)
 
 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/user-guide/starter-guide/switch-to-production)/
+1. Deploy and manage the server manually on [your own cloud](https://docs.zenml.io/user-guide/production-guide/connect-deployed-zenml)/
 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.
 

supabase-openai-summary/README.md

@@ -29,7 +29,7 @@ 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/user-guide/starter-guide/switch-to-production). One of the
+[deploy ZenML](https://docs.zenml.io/user-guide/production-guide/connect-deployed-zenml). 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:

zen-news-summarization/README.md

@@ -127,7 +127,7 @@ 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/user-guide/starter-guide/switch-to-production).
+[the corresponding docs page](https://docs.zenml.io/user-guide/production-guide/connect-deployed-zenml).
 
 ## ZenNews Stack