docs: Fix alignment for workshop doc

adchia · adchia · commit 43480a41bacb · 2022-05-20T15:11:30.000-04:00
Signed-off-by: Danny Chiao &lt;danny@tecton.ai&gt;
diff --git a/docs/getting-started/feast-workshop.md b/docs/getting-started/feast-workshop.md
@@ -1,36 +1,42 @@
+# Learning by example
+
 This workshop aims to teach users about Feast.
 
 We explain concepts & best practices by example, and also showcase how to address common use cases.
 
 ### Pre-requisites
+
 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))
+
+* 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))
 
 Since we'll be learning how to leverage Feast in CI/CD, you'll also need to fork this workshop repository.
 
-**Caveats** 
-- M1 Macbook development is untested with this flow. See also [How to run / develop for Feast on M1 Macs](https://github.com/feast-dev/feast/issues/2105).
-- Windows development has only been tested with WSL. You will need to follow this [guide](https://docs.docker.com/desktop/windows/wsl/) to have Docker play nicely.
+#### **Caveats**
+
+* M1 Macbook development is untested with this flow. See also [How to run / develop for Feast on M1 Macs](https://github.com/feast-dev/feast/issues/2105).
+* Windows development has only been tested with WSL. You will need to follow this [guide](https://docs.docker.com/desktop/windows/wsl/) to have Docker play nicely.
+
+### Modules
 
-## Modules
-*See also: [Feast quickstart](https://docs.feast.dev/getting-started/quickstart), [Feast x Great Expectations tutorial](https://docs.feast.dev/tutorials/validating-historical-features)*
+_See also:_ [_Feast quickstart_](https://docs.feast.dev/getting-started/quickstart)_,_ [_Feast x Great Expectations tutorial_](https://docs.feast.dev/tutorials/validating-historical-features)
 
 These are meant mostly to be done in order, with examples building on previous concepts.
 
-| Time (min) | Description                                                             | Module&nbsp;&nbsp;&nbsp;                                                   |
-| :--------: | :---------------------------------------------------------------------- | :------------------------------------------------------------------------- |
-|   30-45    | Setting up Feast projects & CI/CD + powering batch predictions          | [Module 0](https://github.com/feast-dev/feast-workshop/tree/main/module_0) |
-|   15-20    | Streaming ingestion & online feature retrieval with Kafka, Spark, Redis | [Module 1](https://github.com/feast-dev/feast-workshop/tree/main/module_1) |
-|   10-15    | Real-time feature engineering with on demand transformations            | [Module 2](https://github.com/feast-dev/feast-workshop/tree/main/module_2) |
-|    TBD     | Feature server deployment (embed, as a service, AWS Lambda)             | TBD                                                                        |
-|    TBD     | Versioning features / models in Feast                                   | TBD                                                                        |
-|    TBD     | Data quality monitoring in Feast                                        | TBD                                                                        |
-|    TBD     | Batch transformations                                                   | TBD                                                                        |
-|    TBD     | Stream transformations                                                  | TBD                                                                        |
+| Time (min) | Description                                                             | Module                                                                      |
+| :--------: | ----------------------------------------------------------------------- | --------------------------------------------------------------------------- |
+|    30-45   | Setting up Feast projects & CI/CD + powering batch predictions          | [Module 0](https://github.com/feast-dev/feast-workshop/tree/main/module\_0) |
+|    15-20   | Streaming ingestion & online feature retrieval with Kafka, Spark, Redis | [Module 1](https://github.com/feast-dev/feast-workshop/tree/main/module\_1) |
+|    10-15   | Real-time feature engineering with on demand transformations            | [Module 2](https://github.com/feast-dev/feast-workshop/tree/main/module\_2) |
+|     TBD    | Feature server deployment (embed, as a service, AWS Lambda)             | TBD                                                                         |
+|     TBD    | Versioning features / models in Feast                                   | TBD                                                                         |
+|     TBD    | Data quality monitoring in Feast                                        | TBD                                                                         |
+|     TBD    | Batch transformations                                                   | TBD                                                                         |
+|     TBD    | Stream transformations                                                  | TBD                                                                         |
