Fix broken doc links (#658)

docs/README.md

@@ -16,7 +16,7 @@
 
 [![License](https://img.shields.io/badge/License-Apache%202.0-blue)](https://github.com/linkedin/feathr/blob/main/LICENSE)
 [![GitHub Release](https://img.shields.io/github/v/release/linkedin/feathr.svg?style=flat&sort=semver&color=blue)](https://github.com/linkedin/feathr/releases)
-[![Docs Latest](https://img.shields.io/badge/docs-latest-blue.svg)](https://linkedin.github.io/feathr/)
+[![Docs Latest](https://img.shields.io/badge/docs-latest-blue.svg)](https://feathr-ai.github.io/feathr/)
 [![Python API](https://img.shields.io/readthedocs/feathr?label=Python%20API)](https://feathr.readthedocs.io/en/latest/)
 [![CII Best Practices](https://bestpractices.coreinfrastructure.org/projects/6457/badge)](https://bestpractices.coreinfrastructure.org/projects/6457)
 
@@ -48,16 +48,16 @@ Feathr automatically computes your feature values and joins them to your trainin
 
 Feathr has native integrations with Databricks and Azure Synapse:
 
-Follow the [Feathr ARM deployment guide](https://linkedin.github.io/feathr/how-to-guides/azure-deployment-arm.html) to run Feathr on Azure. This allows you to quickly get started with automated deployment using Azure Resource Manager template.
+Follow the [Feathr ARM deployment guide](https://feathr-ai.github.io/feathr/how-to-guides/azure-deployment-arm.html) to run Feathr on Azure. This allows you to quickly get started with automated deployment using Azure Resource Manager template.
 
-If you want to set up everything manually, you can checkout the [Feathr CLI deployment guide](https://linkedin.github.io/feathr/how-to-guides/azure-deployment-cli.html) to run Feathr on Azure. This allows you to understand what is going on and set up one resource at a time.
+If you want to set up everything manually, you can checkout the [Feathr CLI deployment guide](https://feathr-ai.github.io/feathr/how-to-guides/azure-deployment-cli.html) to run Feathr on Azure. This allows you to understand what is going on and set up one resource at a time.
 
 - Please read the [Quick Start Guide for Feathr on Databricks](./quickstart_databricks.md) to run Feathr with Databricks.
 - Please read the [Quick Start Guide for Feathr on Azure Synapse](./quickstart_synapse.md) to run Feathr with Azure Synapse.
 
 ## 📓 Documentation
 
-- For more details on Feathr, read our [documentation](https://linkedin.github.io/feathr/).
+- For more details on Feathr, read our [documentation](https://feathr-ai.github.io/feathr/).
 - For Python API references, read the [Python API Reference](https://feathr.readthedocs.io/).
 - For technical talks on Feathr, see the [slides here](./talks/Feathr%20Feature%20Store%20Talk.pdf). The recording is [here](https://www.youtube.com/watch?v=gZg01UKQMTY).
 
@@ -149,15 +149,15 @@ user_item_similarity = DerivedFeature(name="user_item_similarity",
 
 ### Define Streaming Features
 
-Read the [Streaming Source Ingestion Guide](https://linkedin.github.io/feathr/how-to-guides/streaming-source-ingestion.html) for more details.
+Read the [Streaming Source Ingestion Guide](https://feathr-ai.github.io/feathr/how-to-guides/streaming-source-ingestion.html) for more details.
 
 ### Point in Time Joins
 
-Read [Point-in-time Correctness and Point-in-time Join in Feathr](https://linkedin.github.io/feathr/concepts/point-in-time-join.html) for more details.
+Read [Point-in-time Correctness and Point-in-time Join in Feathr](https://feathr-ai.github.io/feathr/concepts/point-in-time-join.html) for more details.
 
 ### Running Feathr Examples
 
-Follow the [quick start Jupyter Notebook](./samples/product_recommendation_demo.ipynb) to try it out. There is also a companion [quick start guide](https://linkedin.github.io/feathr/quickstart_synapse.html) containing a bit more explanation on the notebook.
+Follow the [quick start Jupyter Notebook](./samples/product_recommendation_demo.ipynb) to try it out. There is also a companion [quick start guide](https://feathr-ai.github.io/feathr/quickstart_synapse.html) containing a bit more explanation on the notebook.
 
 ## 🗣️ Tech Talks on Feathr
 

docs/concepts/registry-access-control.md

@@ -71,12 +71,12 @@ _AAD Group_ is **NOT** supported yet.
 
 A _Role Assignment_ is the process of add a `user-role` mapping record into backend storage table.
 
-[Feature Registry](https://linkedin.github.io/feathr/concepts/feature-registry.html#access-control-management-page) section briefly introduced the access control management page, where project admins can manage role assignments.
+[Feature Registry](https://feathr-ai.github.io/feathr/concepts/feature-registry.html#access-control-management-page) section briefly introduced the access control management page, where project admins can manage role assignments.
 Management APIs are not exposed in Feathr Client by design. As we don't want to put control plane together with data plane.
 
 ## How to enable Registry Access Control?
 
-[Azure Resource Provisioning](https://linkedin.github.io/feathr/how-to-guides/azure-deployment-arm.html) section has detailed instructions on resource provisioning. For RBAC specific, you will need to manually:
+[Azure Resource Provisioning](https://feathr-ai.github.io/feathr/how-to-guides/azure-deployment-arm.html) section has detailed instructions on resource provisioning. For RBAC specific, you will need to manually:
 
 1. Choose `Yes` for `Enable RBAC` in ARM Template, and provision the resources.
 2. Create a `userrole` table in provisioned SQL database with [RBAC Schema](../../registry/access_control/scripts/schema.sql).

docs/dev_guide/feathr_overall_release_guide.md

@@ -59,10 +59,10 @@ See [Developer Guide for publishing to maven](publish_to_maven.md)
 Run the command to generate the Java jar. After the jar is generated, please upload to [Azure storage](https://ms.portal.azure.com/#view/Microsoft_Azure_Storage/ContainerMenuBlade/~/overview/storageAccountId/%2Fsubscriptions%2Fa6c2a7cc-d67e-4a1a-b765-983f08c0423a%2FresourceGroups%2Fazurefeathrintegration%2Fproviders%2FMicrosoft.Storage%2FstorageAccounts%2Fazurefeathrstorage/path/public/etag/%220x8D9E6F64D62D599%22/defaultEncryptionScope/%24account-encryption-key/denyEncryptionScopeOverride//defaultId//publicAccessVal/Container) for faster access.
 
 ## Release PyPi
-The automated workflow should take care of this, you can check under [actions](https://github.com/linkedin/feathr/actions/workflows/publish-to-pypi.yml) to see the triggered run and results. For manual steps, see [Python Package Release Note](https://linkedin.github.io/feathr/dev_guide/python_package_release.html)
+The automated workflow should take care of this, you can check under [actions](https://github.com/linkedin/feathr/actions/workflows/publish-to-pypi.yml) to see the triggered run and results. For manual steps, see [Python Package Release Note](https://feathr-ai.github.io/feathr/dev_guide/python_package_release.html)
 
 ## Updating docker image for API and Registry
-The automated workflow should take care of this as well, you can check under [actions](https://github.com/linkedin/feathr/actions/workflows/docker-publish.yml) to see the triggered run and results. For manual steps, see [Feathr Registry docker image](https://linkedin.github.io/feathr/dev_guide/build-and-push-feathr-registry-docker-image.html)
+The automated workflow should take care of this as well, you can check under [actions](https://github.com/linkedin/feathr/actions/workflows/docker-publish.yml) to see the triggered run and results. For manual steps, see [Feathr Registry docker image](https://feathr-ai.github.io/feathr/dev_guide/build-and-push-feathr-registry-docker-image.html)
 
 ## Testing
 Run one of the sample [notebook](https://github.com/linkedin/feathr/blob/main/docs/samples/product_recommendation_demo.ipynb) as it uses the latest package from Maven and PyPi.

docs/how-to-guides/azure-deployment-arm.md

@@ -128,7 +128,7 @@ For more details on RBAC, refer to [Feathr Registry Access Control](../how-to-gu
 
 ## Next Steps
 
-Follow the quick start guide [here](https://linkedin.github.io/feathr/quickstart_synapse.html) to try out a notebook example.
+Follow the quick start guide [here](https://feathr-ai.github.io/feathr/quickstart_synapse.html) to try out a notebook example.
 
 ## Known Issues/Workaround
 

docs/quickstart_synapse.md

@@ -22,7 +22,7 @@ First step is to provision required cloud resources if you want to use Feathr. F
 
 Feathr has native cloud integration. Here are the steps to use Feathr on Azure:
 
-1. Follow the [Feathr ARM deployment guide](https://linkedin.github.io/feathr/how-to-guides/azure-deployment-arm.html) to run Feathr on Azure. This allows you to quickly get started with automated deployment using Azure Resource Manager template. Alternatively, if you want to set up everything manually, you can checkout the [Feathr CLI deployment guide](https://linkedin.github.io/feathr/how-to-guides/azure-deployment-cli.html) to run Feathr on Azure. This allows you to understand what is going on and set up one resource at a time.
+1. Follow the [Feathr ARM deployment guide](https://feathr-ai.github.io/feathr/how-to-guides/azure-deployment-arm.html) to run Feathr on Azure. This allows you to quickly get started with automated deployment using Azure Resource Manager template. Alternatively, if you want to set up everything manually, you can checkout the [Feathr CLI deployment guide](https://feathr-ai.github.io/feathr/how-to-guides/azure-deployment-cli.html) to run Feathr on Azure. This allows you to understand what is going on and set up one resource at a time.
 
 2. Once the deployment is complete,run the Feathr Jupyter Notebook by clicking this button: [![Binder](https://mybinder.org/badge_logo.svg)](https://mybinder.org/v2/gh/linkedin/feathr/main?labpath=feathr_project%2Ffeathrcli%2Fdata%2Ffeathr_user_workspace%2Fnyc_driver_demo.ipynb). 
 3. You only need to change the specified `Resource Prefix`.
@@ -188,7 +188,7 @@ client.multi_get_online_features("nycTaxiDemoFeature", ["239", "265"], ['f_locat
 ## Next steps
 
 - Run the [demo notebook](./samples/product_recommendation_demo.ipynb) to understand the workflow of Feathr.
-- Read the [Feathr Documentation Page](https://linkedin.github.io/feathr/) page to understand the Feathr abstractions.
-- Read guide to understand [how to setup Feathr on Azure using Azure Resource Manager template](https://linkedin.github.io/feathr/how-to-guides/azure-deployment-arm.html).
-- Read guide to understand [how to setup Feathr step by step on Azure using Azure CLI](https://linkedin.github.io/feathr/how-to-guides/azure-deployment-cli.html).
+- Read the [Feathr Documentation Page](https://feathr-ai.github.io/feathr/) page to understand the Feathr abstractions.
+- Read guide to understand [how to setup Feathr on Azure using Azure Resource Manager template](https://feathr-ai.github.io/feathr/how-to-guides/azure-deployment-arm.html).
+- Read guide to understand [how to setup Feathr step by step on Azure using Azure CLI](https://feathr-ai.github.io/feathr/how-to-guides/azure-deployment-cli.html).
 - Read [Python API Documentation](https://feathr.readthedocs.io/en/latest/)

docs/samples/customer360/Customer360.ipynb

@@ -61,7 +61,7 @@
  "\n",
  "First step is to provision required cloud resources if you want to use Feathr. Feathr provides a python based client to interact with cloud resources.\n",
  "\n",
- "Please follow the steps [here](https://linkedin.github.io/feathr/how-to-guides/azure-deployment-arm.html) to provision required cloud resources. Due to the complexity of the possible cloud environment, it is almost impossible to create a script that works for all the use cases. Because of this, [azure_resource_provision.sh](https://github.com/linkedin/feathr/blob/main/docs/how-to-guides/azure_resource_provision.sh) is a full end to end command line to create all the required resources, and you can tailor the script as needed, while [the companion documentation](https://linkedin.github.io/feathr/how-to-guides/azure-deployment-cli.html) can be used as a complete guide for using that shell script.\n",
+ "Please follow the steps [here](https://feathr-ai.github.io/feathr/how-to-guides/azure-deployment-arm.html) to provision required cloud resources. Due to the complexity of the possible cloud environment, it is almost impossible to create a script that works for all the use cases. Because of this, [azure_resource_provision.sh](https://github.com/linkedin/feathr/blob/main/docs/how-to-guides/azure_resource_provision.sh) is a full end to end command line to create all the required resources, and you can tailor the script as needed, while [the companion documentation](https://feathr-ai.github.io/feathr/how-to-guides/azure-deployment-cli.html) can be used as a complete guide for using that shell script.\n",
  "\n",
  "\n",
  "And the architecture is as below:\n",

docs/samples/fraud_detection_demo.ipynb

@@ -44,7 +44,7 @@
  "cell_type": "markdown",
  "metadata": {},
  "source": [
- "***Prior to running the notebook, if you have not deployed all the required resources, please refer to the guide here and follow the steps to do so: https://linkedin.github.io/feathr/how-to-guides/azure-deployment-arm.html***"
+ "***Prior to running the notebook, if you have not deployed all the required resources, please refer to the guide here and follow the steps to do so: https://feathr-ai.github.io/feathr/how-to-guides/azure-deployment-arm.html***"
  ]
  },
  {

docs/samples/product_recommendation_demo.ipynb

@@ -21,7 +21,7 @@
  "\n",
  "First step is to provision required cloud resources if you want to use Feathr. Feathr provides a python based client to interact with cloud resources.\n",
  "\n",
- "Please follow the steps [here](https://linkedin.github.io/feathr/how-to-guides/azure-deployment-arm.html) to provision required cloud resources. Due to the complexity of the possible cloud environment, it is almost impossible to create a script that works for all the use cases. Because of this, [azure_resource_provision.sh](https://github.com/linkedin/feathr/blob/main/docs/how-to-guides/azure_resource_provision.sh) is a full end to end command line to create all the required resources, and you can tailor the script as needed, while [the companion documentation](https://linkedin.github.io/feathr/how-to-guides/azure-deployment-cli.html) can be used as a complete guide for using that shell script. \n",
+ "Please follow the steps [here](https://feathr-ai.github.io/feathr/how-to-guides/azure-deployment-arm.html) to provision required cloud resources. Due to the complexity of the possible cloud environment, it is almost impossible to create a script that works for all the use cases. Because of this, [azure_resource_provision.sh](https://github.com/linkedin/feathr/blob/main/docs/how-to-guides/azure_resource_provision.sh) is a full end to end command line to create all the required resources, and you can tailor the script as needed, while [the companion documentation](https://feathr-ai.github.io/feathr/how-to-guides/azure-deployment-cli.html) can be used as a complete guide for using that shell script. \n",
  "\n",
  "\n",
  "![Architecture](https://github.com/linkedin/feathr/blob/main/docs/images/architecture.png?raw=true)"

docs/samples/product_recommendation_demo_advanced.ipynb

@@ -53,7 +53,7 @@
  "\n",
  "First step is to provision required cloud resources if you want to use Feathr. Feathr provides a python based client to interact with cloud resources.\n",
  "\n",
- "Please follow the steps [here](https://linkedin.github.io/feathr/how-to-guides/azure-deployment-arm.html) to provision required cloud resources. Due to the complexity of the possible cloud environment, it is almost impossible to create a script that works for all the use cases. Because of this, [azure_resource_provision.sh](https://github.com/linkedin/feathr/blob/main/docs/how-to-guides/azure_resource_provision.sh) is a full end to end command line to create all the required resources, and you can tailor the script as needed, while [the companion documentation](https://linkedin.github.io/feathr/how-to-guides/azure-deployment-cli.html) can be used as a complete guide for using that shell script. \n",
+ "Please follow the steps [here](https://feathr-ai.github.io/feathr/how-to-guides/azure-deployment-arm.html) to provision required cloud resources. Due to the complexity of the possible cloud environment, it is almost impossible to create a script that works for all the use cases. Because of this, [azure_resource_provision.sh](https://github.com/linkedin/feathr/blob/main/docs/how-to-guides/azure_resource_provision.sh) is a full end to end command line to create all the required resources, and you can tailor the script as needed, while [the companion documentation](https://feathr-ai.github.io/feathr/how-to-guides/azure-deployment-cli.html) can be used as a complete guide for using that shell script. \n",
  "\n",
  "\n",
  "![Architecture](https://github.com/linkedin/feathr/blob/main/docs/images/architecture.png?raw=true)"

feathr_project/feathr/definition/materialization_settings.py

@@ -5,7 +5,7 @@
 
 
 class BackfillTime:
- """Time range to materialize/backfill feature data. Please refer to https://linkedin.github.io/feathr/concepts/materializing-features.html#feature-backfill for a more detailed explanation.
+ """Time range to materialize/backfill feature data. Please refer to https://feathr-ai.github.io/feathr/concepts/materializing-features.html#feature-backfill for a more detailed explanation.
 
  Attributes:
  start: start time of the backfill, inclusive.

feathr_project/feathrcli/data/feathr_user_workspace/nyc_driver_demo.ipynb

@@ -31,7 +31,7 @@
  "## Prerequisite: Use Quick Start Template to Provision Azure Resources\n",
  "First step is to provision required cloud resources if you want to use Feathr. Feathr provides a python based client to interact with cloud resources.\n",
  "\n",
- "Please follow the steps [here](https://linkedin.github.io/feathr/how-to-guides/azure-deployment-arm.html) to provision required cloud resources. Due to the complexity of the possible cloud environment, it is almost impossible to create a script that works for all the use cases. Because of this, [azure_resource_provision.sh](https://github.com/linkedin/feathr/blob/main/docs/how-to-guides/azure_resource_provision.sh) is a full end to end command line to create all the required resources, and you can tailor the script as needed, while [the companion documentation](https://linkedin.github.io/feathr/how-to-guides/azure-deployment-cli.html) can be used as a complete guide for using that shell script.\n",
+ "Please follow the steps [here](https://feathr-ai.github.io/feathr/how-to-guides/azure-deployment-arm.html) to provision required cloud resources. Due to the complexity of the possible cloud environment, it is almost impossible to create a script that works for all the use cases. Because of this, [azure_resource_provision.sh](https://github.com/linkedin/feathr/blob/main/docs/how-to-guides/azure_resource_provision.sh) is a full end to end command line to create all the required resources, and you can tailor the script as needed, while [the companion documentation](https://feathr-ai.github.io/feathr/how-to-guides/azure-deployment-cli.html) can be used as a complete guide for using that shell script.\n",
  "\n",
  "\n",
  "![Architecture](https://github.com/linkedin/feathr/blob/main/docs/images/architecture.png?raw=true)"

ui/src/pages/home/home.tsx

@@ -25,7 +25,7 @@ const Home = () => {
  feature lineages and manage access controls.
  <a
  target="_blank"
- href="https://linkedin.github.io/feathr/concepts/feature-registry.html#accessing-feathr-ui"
+ href="https://feathr-ai.github.io/feathr/concepts/feature-registry.html#accessing-feathr-ui"
  rel="noreferrer"
  >
  {" "}
@@ -228,7 +228,7 @@ const Home = () => {
  <a
  target="_blank"
  rel="noreferrer"
- href="https://linkedin.github.io/feathr/concepts/feathr-concepts-for-beginners.html"
+ href="https://feathr-ai.github.io/feathr/concepts/feathr-concepts-for-beginners.html"
  >
  {" "}
  Feathr Github Homepage