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Update the old Feast installation documentation links #3674

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Update the old Feast installation documentation links #3674

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12 changes: 6 additions & 6 deletions content/en/docs/external-add-ons/feature-store/getting-started.md
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Expand Up @@ -21,12 +21,12 @@ For an overview of Feast, please read [Introduction to Feast](/docs/external-add
**Installation**

To use Feast with Kubeflow, please follow the following steps
* [Install Feast](https://docs.feast.dev/how-to-guides/feast-gcp-aws/install-feast) into your development environment, as well as any environment where you want to register feature views or read features from the feature store.
* [Create a feature repository](https://docs.feast.dev/how-to-guides/feast-gcp-aws/create-a-feature-repository) to store your feature views and entities. Make sure to configure your feature_store.yaml to point to your online store. Pleas see the online store [configuration reference](https://docs.feast.dev/reference/online-stores) here for more details.
* [Deploy your feature store](https://docs.feast.dev/how-to-guides/feast-gcp-aws/deploy-a-feature-store). This step configures your online store and sets up your feature registry.
* [Build a training dataset](https://docs.feast.dev/how-to-guides/feast-gcp-aws/build-a-training-dataset). This step is typically executed from a Kubeflow Pipeline from which you'd train a model.
* [Load features into the online store](https://docs.feast.dev/how-to-guides/feast-gcp-aws/load-data-into-the-online-store). This step can also be executed from a Kubernetes cron job.
* [Read features from the online store](https://docs.feast.dev/how-to-guides/feast-gcp-aws/read-features-from-the-online-store). This step is typically executed from your model serving service, right before calling your model for a prediction.
* [Install Feast](https://docs.feast.dev/how-to-guides/feast-snowflake-gcp-aws/install-feast) into your development environment, as well as any environment where you want to register feature views or read features from the feature store.
* [Create a feature repository](https://docs.feast.dev/how-to-guides/feast-snowflake-gcp-aws/create-a-feature-repository) to store your feature views and entities. Make sure to configure your feature_store.yaml to point to your online store. Pleas see the online store [configuration reference](https://docs.feast.dev/reference/online-stores) here for more details.
* [Deploy your feature store](https://docs.feast.dev/how-to-guides/feast-snowflake-gcp-aws/deploy-a-feature-store). This step configures your online store and sets up your feature registry.
* [Build a training dataset](https://docs.feast.dev/how-to-guides/feast-snowflake-gcp-aws/build-a-training-dataset). This step is typically executed from a Kubeflow Pipeline from which you'd train a model.
* [Load features into the online store](https://docs.feast.dev/how-to-guides/feast-snowflake-gcp-aws/load-data-into-the-online-store). This step can also be executed from a Kubernetes cron job.
* [Read features from the online store](https://docs.feast.dev/how-to-guides/feast-snowflake-gcp-aws/read-features-from-the-online-store). This step is typically executed from your model serving service, right before calling your model for a prediction.

**Advanced**
* Please see [this guide](https://docs.feast.dev/how-to-guides/running-feast-in-production) which provides best practices for running Feast in a production context.
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