Improve Quickstart and Release Guide for v1.0.0 (feathr-ai#1107)

- Update the quickstart guide to make it easier for users to get started, validate feature definitions and develop new things

docs/README.md

@@ -13,8 +13,6 @@
     </h3>
 </html>
 
-
-
 [![License](https://img.shields.io/badge/License-Apache%202.0-blue)](https://github.com/feathr-ai/feathr/blob/main/LICENSE)
 [![GitHub Release](https://img.shields.io/github/v/release/feathr-ai/feathr.svg?style=flat&sort=semver&color=blue)](https://github.com/feathr-ai/feathr/releases)
 [![Docs Latest](https://img.shields.io/badge/docs-latest-blue.svg)](https://feathr-ai.github.io/feathr/)
@@ -56,12 +54,12 @@ The easiest way to try out Feathr is to use the [Feathr Sandbox](https://feathr-
 docker run -it --rm -p 8888:8888 -p 8081:80 -p 7080:7080 -e GRANT_SUDO=yes feathrfeaturestore/feathr-sandbox:releases-v1.0.0
 ```
 
-And you can view default jupyter notebook:
+And you can view Feathr quickstart jupyter notebook:
 ```bash
 http://localhost:8888/lab/workspaces/auto-w/tree/local_quickstart_notebook.ipynb
 ```
 
-After running the Notebooks, all the features will be registered in the UI, and you can visit the Feathr UI at:
+After running the notebook, all the features will be registered in the UI, and you can visit the Feathr UI at:
 
 ```bash
 http://localhost:8081

docs/dev_guide/feathr_overall_release_guide.md

@@ -75,7 +75,7 @@ Once the release branch and release tag are created, the release pipelines will
 
 3. Automated [workflow](https://github.com/feathr-ai/feathr/blob/main/.github/workflows/publish-to-maven.yml) for publishing runtime jar to [maven/sonatype repository](https://oss.sonatype.org/).
 
-4. Automated [workflow](https://github.com/feathr-ai/feathr/blob/main/.github/workflows/publish-fat-jar.yml) for uploading runtime jar 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).
+4. Automated [workflow](https://github.com/feathr-ai/feathr/blob/main/.github/workflows/publish-to-azure-storage.yml) for uploading runtime jar 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).
 
 ## Release PyPI
 

docs/quickstart_local_sandbox.md

@@ -7,12 +7,12 @@ title: Quick Start Guide with Local Sandbox
 
 We provide a local sandbox so users can use Feathr easily. The goal of the Feathr Sandbox is to:
 
-- make it easier for users to get started,
-- make it easy to validate feature definitions and new ideas
-- make it easier for Feathr developers to setup environment and develop new things
+- Make it easier for users to get started,
+- Make it easy to validate feature definitions and new ideas
+- Make it easier for Feathr developers to setup environment and develop new things
 - Interactive experience, usually try to run a job takes less than 1 min.
 
-As an end user, you can become productive in less than 5 mins and try out Feathr.
+As an end user, you can become productive in less than 5 minutes and try out Feathr.
 
 The Sandbox is ideal for:
 
@@ -21,39 +21,47 @@ The Sandbox is ideal for:
 
 ## Getting Started
 
-First, make sure you have docker installed successfully on your machine by running this command:
+### Prerequisites
+
+Make sure Docker is installed on your machine. You can find official installation guide [here]. (https://docs.docker.com/get-docker/#supported-platforms). To make sure Docker is successfully installed on your machine, run a test container as follows:
 
 ```bash
 docker run hello-world
 ```
 
+You should see the `Hello from Docker!` message.
+
+### Step 1: Start Sandbox
+
 To get started using Feathr, simply run the command below. Note that the image is around 5GB so it might take a while to pull it from DockerHub.
 
 ```bash
 # 80: Feathr UI, 8888: Jupyter, 7080: Interpret
 docker run -it --rm -p 8888:8888 -p 8081:80 -p 7080:7080 -e GRANT_SUDO=yes feathrfeaturestore/feathr-sandbox:releases-v1.0.0
 ```
 
-It should pop up a Jupyter link in `http://127.0.0.1:8888/`. Double click on the notebook file to start the Jupyter Notebook, and you should be able to see the Feathr sample notebook. Click the triangle button on the Jupyter notebook and the whole notebook will run locally.
 
-If you see some errors like below, simply change the `-p 8081:80` part to `-p <new port>:80` so the Feathr UI will be redirected to the new port.
+If you see errors like below, simply change the `-p 8081:80` part to `-p <new port>:80` so the Feathr UI will be redirected to the new port.
 
 `docker: Error response from daemon: driver failed programming external connectivity on endpoint hardcore_bose (ae107e924cddce6b942f96f2654369345b027ac82e5e44929a9f132e2af71746): Bind for 0.0.0.0:8081 failed: port is already allocated.`
 
-The default jupyter notebook is here:
+### Step 2: Run Quickstart Sample Notebooks
+
+Open jupyter notebook with below link and click the triangle button on the Jupyter notebook and the whole notebook will run locally.
 ```bash
 http://localhost:8888/lab/workspaces/auto-w/tree/local_quickstart_notebook.ipynb
 ```
 
 ![Feathr Notebook](./images/feathr-sandbox.png)
 
+### Step 3: Browse Features in Feathr UI
 After running the Notebooks, all the features will be registered in the UI, and you can visit the Feathr UI at:
 
 ```bash
 http://localhost:8081
 ```
 
-After executing those scripts, you should be able to see a project called `local_spark` in the Feathr UI. You can also view lineage in the Feathr UI and explore all the details.
+You should be able to see a project called `product_recommendation` created in the Feathr UI. You can also view lineage in the Feathr UI and explore all the details.
 ![Feathr UI](./images/feathr-sandbox-ui.png)
 
 ![Feathr UI](./images/feathr-sandbox-lineage.png)
@@ -64,21 +72,12 @@ The Feathr sandbox comes with:
 - Built-in Jupyter Notebook
 - Pre-installed data science packages such as `interpret` so that data science development becomes easy
 - Pre-installed Feathr package
-- A local spark environment for dev/test purpose
-- Feathr samples that can run locally
-- A local Feathr registry backed by SQLite
+- Local spark environment for dev/test purpose
+- Feathr quickstart notebook sample that can run locally
 - Feathr UI
-- Feathr Registry API
+- Local Feathr Registry API server backed by SQLite
 - Local Redis server
 
-## Build Feathr Sandbox Docker Image
-
-If you want to build the Feathr sandbox, run the below command in the Feathr root directory:
-
-```bash
-docker build -f FeathrSandbox.Dockerfile -t feathrfeaturestore/feathr-sandbox .
-```
-
 ## Configuring Feathr Registry backend in Feathr Sandbox
 
 By default, Feathr Sandbox uses a SQLite backend in Feathr registry, and the content will be deleted if you restart the container. If you want to use a persistent registry, say a remote MySQL database or SQL Server, you can configure an environment variable when starting the Feathr Sandbox container like below, by specifying `FEATHR_SANDBOX_REGISTRY_URL` environment variable:
@@ -109,4 +108,12 @@ In the future, an VSCode Server might be installed so that you can do interactiv
 
 ### Feathr UI and Feathr Registry API Server development
 
-When packaging the Feathr Sandbox docker image, Feathr automatically builds the latest UI and Registry server code, and put them into the right folder of the Feathr Sandbox images.
+When packaging the Feathr Sandbox docker image, Feathr automatically builds the latest UI and Registry server code, and put them into the right folder of the Feathr Sandbox images.
+
+### Build Feathr Sandbox Docker Image
+
+If you want to build the Feathr sandbox, run the below command in the Feathr root directory:
+
+```bash
+docker build -f FeathrSandbox.Dockerfile -t feathrfeaturestore/feathr-sandbox .
+```

docs/release-announcements/v1.0.0.md

@@ -3,18 +3,17 @@ layout: default
 title: Announce Feathr 1.0.0
 ---
 
-Announce Feathr 1.0.0
+# Announce Feathr 1.0.0
 
-Feathr 1.0.0-rc1 is released on February 16, 2023 with following new features
+Feathr 1.0.0 is released on February 28, 2023, with following new features
 
-New Features:
+## New Features:
 
 - Feathr sandbox: The Feathr sandbox is a pre-configured environment that you can use to learn how to use Feathr, experiment with features, and build proof-of-concept applications locally, without setting up complex infrastructure on the cloud. Please check out [Quick Start Guide with Local Sandbox](https://feathr-ai.github.io/feathr/quickstart_local_sandbox.html) to get started.
 
 - Online Transform: Online Transform is a feature that enables real-time feature transformations as part of the feature retrieval process, here are some use cases examples:
 
-  - Featurization source is only available at inference time​.
-
+  - Featurization source is only available at inference time.
   - Using offline transform might be a waste of storage and compute resources.
   - Users would like to decouple featurization work off up-stream online system.
   - Users would like to define transformation once for both online and offline consumption.  
@@ -45,10 +44,10 @@ New Features:
 
 - Use SparkSQL as DataSource, code sample can be found at https://github.com/feathr-ai/feathr/blob/main/feathr_project/test/test_spark_sql_source.py
 
-- Time pattern support in data source path. Please check [Input File for Feathr](https://feathr-ai.github.io/feathr/how-to-guides/feathr-input-format.html#timepartitionpattern-for-input-files) for more details.
+- Time pattern support in data source path. Please check [Input File for Feathr](https://feathr-ai.github.io/feathr/how-to-guides/feathr-input-format.html#timepartitionpattern-for-input-files) for more details.
 
 - Feature names conflicts check and auto correction, please check [Getting Offline Features using Feature Query](https://feathr-ai.github.io/feathr/concepts/get-offline-features.html#feature-names-conflicts-check) for more details.
 
-- Feathr MLOps V2 integration with Terrarform Deployment. Please see [Feathr MLOps V2 Integration Deployment Guide](https://github.com/Azure/mlops-v2/blob/feature/feathr/documentation/deployguides/deployguide_fs_ado.md) for more details.
+- Feathr MLOps V2 integration with Terraform Deployment. Please see [Feathr MLOps V2 Integration Deployment Guide](https://github.com/Azure/mlops-v2/blob/feature/feathr/documentation/deployguides/deployguide_fs_ado.md) for more details.
 
 For more information, please refer to the official Feathr documentation.