add READMEs

Signed-off-by: Danny Chiao <danny@tecton.ai>

docs/reference/feature-servers/python-feature-server.md

@@ -10,7 +10,7 @@ There is a CLI command that starts the server: `feast serve`. By default, Feast
 
 ## Deploying as a service
 
-One can deploy a feature server by building a docker image that bundles in the project's `feature_store.yaml`. See this [helm chart](https://github.com/feast-dev/feast/blob/master/infra/charts/feast-python-server) for an example on how to run Feast on Kubernetes.
+One can deploy a feature server by building a docker image that bundles in the project's `feature_store.yaml`. See this [helm chart](https://github.com/feast-dev/feast/blob/master/infra/charts/feast-feature-server) for an example on how to run Feast on Kubernetes.
 
 A [remote feature server](alpha-aws-lambda-feature-server.md) on AWS Lambda is also available.
 

sdk/python/feast/templates/aws/README.md

@@ -0,0 +1,19 @@
+# Feast Quickstart
+A quick view of what's in this repository:
+
+* `data/` contains raw demo parquet data
+* `example_repo.py` contains demo feature definitions
+* `feature_store.yaml` contains a demo setup configuring where data sources are
+* `test_workflow.py` showcases how to run all key Feast commands, including defining, retrieving, and pushing features. 
+
+You can run the overall workflow with `python test_workflow.py`.
+
+## To move from this into a more production ready workflow:
+1. `feature_store.yaml` points to a local file as a registry. You'll want to setup a remote file (e.g. in S3/GCS) or a 
+SQL registry. See [registry docs](https://docs.feast.dev/getting-started/concepts/registry) for more details.
+2. Setup CI/CD + dev vs staging vs prod environments to automatically update the registry as you change Feast feature definitions. See [docs](https://docs.feast.dev/how-to-guides/running-feast-in-production#1.-automatically-deploying-changes-to-your-feature-definitions).
+3. (optional) Regularly scheduled materialization to power low latency feature retrieval (e.g. via Airflow). See [Batch data ingestion](https://docs.feast.dev/getting-started/concepts/data-ingestion#batch-data-ingestion)
+for more details.
+4. (optional) Deploy feature server instances with `feast serve` to expose endpoints to retrieve online features.
+   - See [Python feature server](https://docs.feast.dev/reference/feature-servers/python-feature-server) for details.
+   - Use cases can also directly call the Feast client to fetch features as per [Feature retrieval](https://docs.feast.dev/getting-started/concepts/feature-retrieval)

sdk/python/feast/templates/gcp/README.md

@@ -9,15 +9,13 @@ A quick view of what's in this repository:
 You can run the overall workflow with `python test_workflow.py`.
 
 ## To move from this into a more production ready workflow:
-1. First: you should start with a different Feast template, which delegates to a more scalable offline store. 
-   - For example, running `feast init -t gcp`
-   or `feast init -t aws` or `feast init -t snowflake`. 
-   - You can see your options if you run `feast init --help`.
-2. `feature_store.yaml` points to a local file as a registry. You'll want to setup a remote file (e.g. in S3/GCS) or a 
+1. `feature_store.yaml` points to a local file as a registry. You'll want to setup a remote file (e.g. in S3/GCS) or a 
 SQL registry. See [registry docs](https://docs.feast.dev/getting-started/concepts/registry) for more details. 
-3. This example uses a file [offline store](https://docs.feast.dev/getting-started/architecture-and-components/offline-store) 
-   to generate training data. It does not scale. We recommend instead using a data warehouse such as BigQuery, 
-   Snowflake, Redshift. There is experimental support for Spark as well.
-4. Setup CI/CD + dev vs staging vs prod environments to automatically update the registry as you change Feast feature definitions. See [docs](https://docs.feast.dev/how-to-guides/running-feast-in-production#1.-automatically-deploying-changes-to-your-feature-definitions).
-5. (optional) Regularly scheduled materialization to power low latency feature retrieval (e.g. via Airflow). See [Batch data ingestion](https://docs.feast.dev/getting-started/concepts/data-ingestion#batch-data-ingestion)
-for more details.
+2. This example uses an already setup BigQuery Feast data warehouse as the [offline store](https://docs.feast.dev/getting-started/architecture-and-components/offline-store) 
+   to generate training data. You'll need to connect your own BigQuery instance to make this work.
+3. Setup CI/CD + dev vs staging vs prod environments to automatically update the registry as you change Feast feature definitions. See [docs](https://docs.feast.dev/how-to-guides/running-feast-in-production#1.-automatically-deploying-changes-to-your-feature-definitions).
+4. (optional) Regularly scheduled materialization to power low latency feature retrieval (e.g. via Airflow). See [Batch data ingestion](https://docs.feast.dev/getting-started/concepts/data-ingestion#batch-data-ingestion)
+for more details. 
+5. (optional) Deploy feature server instances with `feast serve` to expose endpoints to retrieve online features.
+   - See [Python feature server](https://docs.feast.dev/reference/feature-servers/python-feature-server) for details.
+   - Use cases can also directly call the Feast client to fetch features as per [Feature retrieval](https://docs.feast.dev/getting-started/concepts/feature-retrieval)

sdk/python/feast/templates/local/README.md

@@ -21,4 +21,7 @@ SQL registry. See [registry docs](https://docs.feast.dev/getting-started/concept
    Snowflake, Redshift. There is experimental support for Spark as well.
 4. Setup CI/CD + dev vs staging vs prod environments to automatically update the registry as you change Feast feature definitions. See [docs](https://docs.feast.dev/how-to-guides/running-feast-in-production#1.-automatically-deploying-changes-to-your-feature-definitions).
 5. (optional) Regularly scheduled materialization to power low latency feature retrieval (e.g. via Airflow). See [Batch data ingestion](https://docs.feast.dev/getting-started/concepts/data-ingestion#batch-data-ingestion)
-for more details.
+for more details.
+6. (optional) Deploy feature server instances with `feast serve` to expose endpoints to retrieve online features.
+   - See [Python feature server](https://docs.feast.dev/reference/feature-servers/python-feature-server) for details.
+   - Use cases can also directly call the Feast client to fetch features as per [Feature retrieval](https://docs.feast.dev/getting-started/concepts/feature-retrieval)

sdk/python/feast/templates/snowflake/README.md

@@ -0,0 +1,19 @@
+# Feast Quickstart
+A quick view of what's in this repository:
+
+* `data/` contains raw demo parquet data
+* `example_repo.py` contains demo feature definitions
+* `feature_store.yaml` contains a demo setup configuring where data sources are
+* `test_workflow.py` showcases how to run all key Feast commands, including defining, retrieving, and pushing features.
+
+You can run the overall workflow with `python test_workflow.py`.
+
+## To move from this into a more production ready workflow:
+1. `feature_store.yaml` points to a local file as a registry. You'll want to setup a remote file (e.g. in S3/GCS) or a
+   SQL registry. See [registry docs](https://docs.feast.dev/getting-started/concepts/registry) for more details.
+2. Setup CI/CD + dev vs staging vs prod environments to automatically update the registry as you change Feast feature definitions. See [docs](https://docs.feast.dev/how-to-guides/running-feast-in-production#1.-automatically-deploying-changes-to-your-feature-definitions).
+3. (optional) Regularly scheduled materialization to power low latency feature retrieval (e.g. via Airflow). See [Batch data ingestion](https://docs.feast.dev/getting-started/concepts/data-ingestion#batch-data-ingestion)
+   for more details.
+4. (optional) Deploy feature server instances with `feast serve` to expose endpoints to retrieve online features. 
+    - See [Python feature server](https://docs.feast.dev/reference/feature-servers/python-feature-server) for details. 
+    - Use cases can also directly call the Feast client to fetch features as per [Feature retrieval](https://docs.feast.dev/getting-started/concepts/feature-retrieval)