Call out different requirements for GCP in module 0

adchia · adchia · commit 208f6f6af3cf · 2022-06-08T12:17:05.000-04:00
Signed-off-by: Danny Chiao &lt;danny@tecton.ai&gt;
diff --git a/README.md b/README.md
@@ -21,12 +21,19 @@ Feast solves several common challenges teams face:
 This workshop assumes you have the following installed:
 - A local development environment that supports running Jupyter notebooks (e.g. VSCode with Jupyter plugin)
 - Python 3.7+
-- Java 11 (for Spark, e.g. `brew install java11`)
 - pip
-- Docker & Docker Compose (e.g. `brew install docker docker-compose`)
-- Terraform ([docs](https://learn.hashicorp.com/tutorials/terraform/install-cli#install-terraform))
-- AWS CLI
-- An AWS account setup with credentials via `aws configure` (e.g see [AWS credentials quickstart](https://docs.aws.amazon.com/cli/latest/userguide/cli-configure-quickstart.html#cli-configure-quickstart-creds))
+  - Docker & Docker Compose (e.g. `brew install docker docker-compose`)
+- **Module 0 pre-requisites**:
+  - Terraform ([docs](https://learn.hashicorp.com/tutorials/terraform/install-cli#install-terraform))
+  - Either AWS or GCP setup:
+    - AWS
+      - AWS CLI
+      - An AWS account setup with credentials via `aws configure` (e.g see [AWS credentials quickstart](https://docs.aws.amazon.com/cli/latest/userguide/cli-configure-quickstart.html#cli-configure-quickstart-creds))
+    - GCP
+      - GCP account
+      - `gcloud` CLI
+- **Module 1 pre-requisites**:
+  - Java 11 (for Spark, e.g. `brew install java11`)
 
 Since we'll be learning how to leverage Feast in CI/CD, you'll also need to fork this workshop repository.
 
diff --git a/module_0/README.md b/module_0/README.md
@@ -110,7 +110,7 @@ It's worth noting that there are multiple types of feature views. `OnDemandFeatu
 There are three user groups here worth considering. The ML platform team, the ML engineers running batch inference on models, and the data scientists building models. 
 
 ## User group 1: ML Platform Team
-The team here sets up the centralized Feast feature repository and CI/CD in GitHub. This is what's seen in `feature_repo_aws/`.
+The team here sets up the centralized Feast feature repository and CI/CD in GitHub. This is what's seen in `feature_repo_aws/` or `feature_repo_gcp/`.
 
 ### Step 0 (AWS): Setup S3 bucket for registry and file sources
 This assumes you have an AWS account & Terraform setup. If you don't:
@@ -639,7 +639,7 @@ We don't need to do anything new here since data scientists will be doing many o
 
 There are two ways data scientists can use Feast:
 - Use Feast primarily as a way of pulling production ready features. 
-  - See the `client_aws/` or `client_no_yaml` folders for examples of how users can pull features by only having a `feature_store.yaml` or instantiating a `RepoConfig` object
+  - See the `client_aws/`/`client_gcp/`, or `client_no_yaml` folders for examples of how users can pull features by only having a `feature_store.yaml` or instantiating a `RepoConfig` object
   - This is **not recommended** since data scientists cannot register feature services to indicate they depend on certain features in production. 
 - **[Recommended]** Have a local copy of the feature repository (e.g. `git clone`) and author / iterate / re-use features. 
   - Data scientist can:
