clean up copy and update learn more page

Signed-off-by: Niels Bantilan <[email protected]>

markdown/step_01.md

@@ -18,7 +18,7 @@ dependencies for our workflow. You can think of this as a high-level interface
 to define a container image:
 
 ```python
-image = fl.ImageSpec(packages=["pandas", "pyarrow", "scikit-learn"])
+image = union.ImageSpec(packages=["pandas", "pyarrow", "scikit-learn"])
 ```
 
 Then, we define the `get_data` and `train_model` tasks and compose them
@@ -56,19 +56,19 @@ def training_workflow(
 ```
 
 Union supports seamless development from local to remote execution.
-Run this workflow locally with the `union run` CLI command:
+Run this workflow on the Workspace machine itself with the `union run` CLI command:
 
-[▶️ Run locally in terminal](command:union-workspace.runTrainingLocal)
+[▶️ Run locally in the Workspace](command:union-workspace.runTrainingLocal)
 
 ```bash
 union run workflows/train.py training_workflow --max_iter 100
 ```
 
-Note that you can pass arguments to the workflow as flags.
+This is useful for quickly debugging your workflow in the local environment.
 
-Then, to run this on Union Serverless, simply supply the `--remote` flag:
+To run this on Union Serverless, simply supply the `--remote` flag:
 
-[▶️ Run remotely in terminal](command:union-workspace.runTrainingRemote)
+[▶️ Run remotely on the Union cluster](command:union-workspace.runTrainingRemote)
 
 
 ```bash

markdown/step_02.md

@@ -4,9 +4,9 @@
 
 # 🔀 Parallelize Model Training
 
-Union allows you to horizontally scale your workflows with `map_task`s. To
-make this more concrete, we can define the hyperparameters of the model training
-step as a dataclass and refactor the `train_model` task from the previous step:
+Union horizontally scales your workflows with `map_task`s. To make this more
+concrete, we can define the hyperparameters of the model training step as a
+dataclass and refactor the `train_model` task from the previous step:
 
  ```python
 from dataclasses import dataclass asdict
@@ -40,7 +40,7 @@ def training_workflow(hp_grid: list[Hyperparameters]) -> list[MLPClassifier]:
 
 Run this workflow by passing a list of hyperparameters to the workflow:
 
-[▶️ Run remotely in terminal](command:union-workspace.runParallelizeWorkflow)
+[▶️ Run remotely on the Union cluster](command:union-workspace.runParallelizeWorkflow)
 
 ```bash
 union run --remote workflows/parallelize.py training_workflow --hp_grid '[{"max_iter": 25}, {"max_iter": 500}, {"max_iter": 100}]'

markdown/step_04.md

@@ -19,3 +19,29 @@ You've successfully run your first Union workflows:
 Use this workspace environment to learn more about Union by checking out the
 [User Guide](https://docs.union.ai/serverless/user-guide/) and end-to-end
 [Tutorial](https://docs.union.ai/serverless/tutorials/) examples.
+
+Clone the Union examples repository to explore and run them:
+
+```
+git clone https://github.com/unionai/unionai-examples
+cd unionai-examples
+```
+
+This repository contains `user_guide` and `tutorials` subfolders that correspond
+to the [User Guide](https://docs.union.ai/serverless/user-guide/) and
+[Tutorial](https://docs.union.ai/serverless/tutorials/) documentation, respectively.
+
+For instance, to run the `user_guide` example [here](https://github.com/unionai/unionai-examples/blob/main/user_guide/first_workflow/ml_workflow/ml_workflow.py),
+run the following command:
+
+```
+union run --remote user_guide/first_workflow/ml_workflow/ml_workflow.py main --max_bins 64
+```
+
+And to run the `tutorials` examples, simply run the instructions in the **Run on Union BYOC** dropdown.
+For example, to train a [credit default model with XGBoost](https://docs.union.ai/byoc/tutorials/finance/credit-default-xgboost),
+run the following command:
+
+```
+union run --remote tutorials/credit_default/credit_default.py credit_default_wf
+```

package.json

@@ -2,7 +2,7 @@
   "name": "union-workspace",
   "displayName": "Union Workspace Onboarding",
   "description": "Visual Studio Code onboarding experience for Union Workspaces",
-  "version": "0.0.10",
+  "version": "0.0.11",
   "publisher": "unionai",
   "engines": {
     "vscode": "^1.71.0"