slashml · docs-alot · Aug 7, 2025 · Aug 7, 2025 · Aug 7, 2025
diff --git a/concepts/deployment.mdx b/concepts/deployment.mdx
@@ -202,10 +202,9 @@ Choose your instance type based on your model's requirements:
 4. Set up monitoring and alerting for your endpoints
 
 <Warning>
-Make sure you setup budget monitory and alerts to avoid unexpected charges.
+Make sure you set up budget monitoring and alerts to avoid unexpected charges.
 </Warning>
 
-
 ## Troubleshooting Deployments
 
 Common issues and their solutions:
@@ -225,4 +224,44 @@ Common issues and their solutions:
    - Verify model ID and version
    - Check instance memory requirements
    - Validate Hugging Face token if required
-   - Endpoing deployed but deployment failed. Check the logs, and do report this to us if you see this issue.
+   - Endpoint deployed but deployment failed. Check the logs, and do report this to us if you see this issue.
+
+---
+
+### Additional Notes on Command-Line Options
+
+The Magemaker CLI supports both interactive and YAML-based deployments. Here’s a breakdown of relevant options:
+
+#### Cloud selection
+```sh
+magemaker --cloud [aws|gcp|azure|all]
+```
+- Launches the interactive deployment flow, walking you through options and configuration for your selected cloud.
+
+#### YAML-based deployment
+```sh
+magemaker --deploy path/to/deployment.yaml
+```
+- Deploys using the specified YAML file. The `destination` field in the YAML chooses the cloud provider.
+
+#### Advanced options
+
+| Flag         | Description                                    |
+|--------------|------------------------------------------------|
+| `--hf`       | Deploy a Hugging Face Model (and select instance interactively) |
+| `--instance` | Specify cloud instance type (overrides YAML if interactive) |
+| `--deploy`   | Path to deployment YAML file                   |
+| `--train`    | Path to training YAML file                     |
+| `--cloud`    | Specify provider for interactive mode          |
+| `-v` / `--verbose` | Increase output verbosity               |
+| `--version`  | Show Magemaker version                         |
+
+> **Note:** If you specify both `--cloud` and `--deploy`, Magemaker will use the `destination` from the YAML and issue a warning if both are provided.
+
+<Warning>
+If you run Magemaker with no arguments, you must specify a cloud provider using `--cloud`. Examples:
+- `magemaker --cloud gcp`
+- `magemaker --cloud aws`
+- `magemaker --cloud azure`
+- `magemaker --cloud all`
+</Warning>
diff --git a/concepts/models.mdx b/concepts/models.mdx
@@ -43,6 +43,7 @@ We plan to add support for the following model sources:
     Models from Azure ML Model Catalog and Azure OpenAI
   </Card>
 </CardGroup>
+
 ## Model Requirements
 
 ### Instance Type Recommendations by Cloud Provider
@@ -131,22 +132,20 @@ deployment: !Deployment
 <Note>
   The model ids for Azure are different from AWS and GCP. Make sure to use the one provided by Azure in the Azure Model Catalog. 
 
-  To find the relevnt model id, follow the following steps
+  To find the relevant model id, follow these steps:
   <Steps>
-    <Step title="Go to your workpsace studio">
-      Find the workpsace in the Azure portal and click on the studio url provided. Click on the `Model Catalog` on the left side bar
+    <Step title="Go to your workspace studio">
+      Find the workspace in the Azure portal and click on the studio URL provided. Click on the `Model Catalog` on the left sidebar.
     	![Azure ML Creation](../Images/workspace-studio.png)
     </Step>
 
-      <Step title="Select Hugging Face in the Collections List">
-      Select Hugging-Face from the collections list. The id of the model card is the id you need to use in the yaml file
+    <Step title="Select Hugging Face in the Collections List">
+      Select Hugging-Face from the collections list. The id of the model card is the id you need to use in the yaml file.
     	![Azure ML Creation](../Images/hugging-face.png)
     </Step>
-
   </Steps>
 </Note>
 
-
 ## Model Configuration
 
 ### Basic Parameters
@@ -181,7 +180,7 @@ models:
    - Consider data residency requirements
    - Test latency from different regions
 
-3. **Cost Management**
+2. **Cost Management**
    - Compare instance pricing
    - Make sure you set up the relevant alerting
 
@@ -202,4 +201,4 @@ Common model-related issues:
 3. **Authentication Issues**
    - Verify cloud credentials
    - Check model access permissions
-   - Validate API keys
+   - Validate API keys
diff --git a/quick-start.mdx b/quick-start.mdx
@@ -128,7 +128,6 @@ models:
 </Note>
 
 
-
 ### Model Fine-tuning
 
 Fine-tune models using the `train` command:
@@ -150,7 +149,8 @@ training: !Training
     per_device_train_batch_size: 32
     learning_rate: 2e-5
 ```
-{/* 
+
+<!-- 
 ### Recommended Models
 
 <CardGroup>
@@ -169,7 +169,8 @@ training: !Training
     Feature extraction: turns text into a 384d vector embedding for semantic
     search / clustering. Query format: "type out a sentence like this one."
   </Card>
-</CardGroup> */}
+</CardGroup> 
+-->
 
 <Warning>
   Remember to deactivate unused endpoints to avoid unnecessary charges!
@@ -184,3 +185,32 @@ You can reach us, faizan & jneid, at [support@slashml.com](mailto:support@slashm
 If anything doesn't make sense or you have suggestions, do point them out at [magemaker.featurebase.app](https://magemaker.featurebase.app/).
 
 We'd love to hear from you! We're excited to learn how we can make this more valuable for the community and welcome any and all feedback and suggestions.
+
+---
+
+### v0.1.2+ CLI Update
+
+**New:** You can now specify `--cloud all` to configure AWS, GCP, and Azure interactively, or use YAML with a `deployment.destination` field for explicit cloud targeting.
+
+#### Cloud CLI flags
+
+- `--cloud aws` — configure for AWS SageMaker
+- `--cloud gcp` — configure for Google Cloud Vertex AI
+- `--cloud azure` — configure for Azure ML
+- `--cloud all` — configure all three providers at once
+
+If you do not specify `--cloud`, the CLI will require you to choose one, and prints a help message.
+
+If you provide a YAML deployment file with `--deploy`, the CLI uses the `deployment.destination` field to determine which provider to target.
+
+**Legacy:** Previous CLI usage with e.g. `magemaker --cloud=gcp` is still supported, but the preferred usage is now:
+
+```sh
+magemaker --cloud gcp
+```
+or
+```sh
+magemaker --deploy .magemaker_config/my-deploy.yaml
+```
+
+If you run just `magemaker` with no arguments, you’ll now see a colored error indicating you must specify a cloud provider, with suggested commands.