Updated as per recommendations

docs/code/aws/cloud_vm.md

@@ -1,7 +1,6 @@
-`cloud_vm` deploys MLOps `stack` on top of Cloud provider VMs.
-# ☁️ Cloud Infrastructure Examples
+# ☁️ Cloud VM Examples
 
-Welcome to the **Cloud Infrastructure** examples! This section contains ready-to-use configurations to help you deploy scalable MLOps stacks on cloud platforms like AWS. Each example is designed to showcase a different tool or workflow, making it easier for you to find and deploy the infrastructure that fits your needs.
+Welcome to the **Cloud VM** examples! This section contains ready-to-use configurations to help you deploy scalable MLOps stacks on cloud platforms like AWS. Each example is designed to showcase a different tool or workflow, making it easier for you to find and deploy the infrastructure that fits your needs.
 
 ## 🚀 Getting Started
 
@@ -26,16 +25,16 @@ Before you begin, ensure you have the following:
 3. **Choose an Example**:
     Navigate to the example you want to deploy:
     ```bash
-    cd examples/cloud_infra/dagster
+    cd examples/cloud_vm/dagster
     ```
 
 4. **Configure AWS Credentials**:
-    Modify the `terraform.tfvars` file to update your AWS account details. Make sure your AWS credentials are correctly configured.
+    Modify the YAML configuration file (e.g., `aws-dagster.yaml`) to update your AWS account details. Ensure your AWS credentials are correctly configured.
 
 5. **Deploy**:
     Use `mlinfra` to apply the configuration:
     ```bash
-    mlinfra terraform --action apply --stack-config-path ./config/aws-config.yaml
+    mlinfra terraform --action apply --stack-config-path examples/cloud_vm/dagster/aws-dagster.yaml
     ```
 
 ## 📂 Examples Available
@@ -86,29 +85,29 @@ Before you begin, make sure you have:
 To deploy the basic infrastructure, use the following command:
 
 ```bash
-aws cloudformation deploy --template-file aws-complete.yaml --stack-name basic-aws-stack
+aws cloudformation deploy --template-file aws-complete.yaml --stack-name aws-mlops-stack-complete
 ```
 
 ### 3. Advanced Deployment
 
 For a more comprehensive setup with additional features, execute:
 
 ```bash
-aws cloudformation deploy --template-file aws-complete-advanced.yaml --stack-name advanced-aws-stack
+mlinfra terraform --action apply --stack-config-path examples/cloud_vm/complete/aws-complete-advanced.yaml
 ```
 
 ### 4. Verifying Deployment
 
 To verify that your infrastructure has been set up correctly, check the status of your CloudFormation stack:
 
 ```bash
-aws cloudformation describe-stacks --stack-name basic-aws-stack
+aws cloudformation describe-stacks --stack-name aws-mlops-stack-complete
 ```
 
 Or for the advanced setup:
 
 ```bash
-aws cloudformation describe-stacks --stack-name advanced-aws-stack
+aws cloudformation describe-stacks --stack-name aws-mlops-stack-complete-advanced
 ```
 
 ### 5. Managing Resources
@@ -149,12 +148,12 @@ To use these configurations, ensure you have the following prerequisites:
 
 ### 📦 Installation
 
-To install the `platinfra` package, you can use pip. It's recommended to create a Python virtual environment first:
+To install the `mlinfra` package, you can use pip. It's recommended to create a Python virtual environment first:
 
 ```bash
 python -m venv venv
 source venv/bin/activate  # On Windows use `venv\Scripts\activate`
-pip install platinfra
+pip install mlinfra
 ```
 
 ## 🌐 Deployment Instructions
@@ -166,7 +165,7 @@ The `aws-lakefs.yaml` file provides a straightforward setup for deploying LakeFS
 **To deploy:** 
 
 ```bash
-platinfra terraform --action apply --stack-config-path aws-lakefs.yaml
+mlinfra terraform --action apply --stack-config-path examples/cloud_vm/lakefs/aws-lakefs.yaml
 ```
 
 This command will create the necessary AWS resources and deploy LakeFS.
@@ -182,26 +181,24 @@ The `aws-lakefs-advanced.yaml` file contains a more comprehensive configuration,
 **To deploy:** 
 
 ```bash
-platinfra terraform --action apply --stack-config-path aws-lakefs-advanced.yaml
+mlinfra terraform --action apply --stack-config-path examples/cloud_vm/lakefs/aws-lakefs-advanced.yaml
 ```
 
-Feel free to modify the advanced configuration according to your specific use cases and resource requirements.
-
 ## 🔧 Customization Tips
 
 - **IAM Roles**: Ensure that the necessary IAM roles and policies are in place to allow LakeFS to access required AWS resources, such as S3.
 - **Environment Variables**: Adjust environment variables in the YAML files for configuring LakeFS settings.
-- **Storage Configuration**: Make sure to configure persistent storage options if needed.
+- **Storage Configuration**: Configure persistent storage options as needed.
 
 ## 📦 Managing Your Deployment
 
 - **Update Configuration**: To apply changes, use:
   ```bash
-  platinfra terraform --action apply --stack-config-path <your-updated-file>.yaml
+  mlinfra terraform --action apply --stack-config-path <your-updated-file>.yaml
   ```
-- **Check Status**: Monitor the deployment using:
+- **Check Status**: Monitor the deployment status using:
   ```bash
-  aws cloudformation describe-stacks --stack-name lakefs-stack
+  aws cloudformation describe-stacks --stack-name aws-mlops-stack-lakefs
   ```
 - **Logs**: View logs for troubleshooting in the AWS Console.
 
@@ -210,8 +207,8 @@ Feel free to modify the advanced configuration according to your specific use ca
 To remove the LakeFS deployment, execute:
 
 ```bash
-platinfra terraform --action destroy --stack-config-path aws-lakefs.yaml
-platinfra terraform --action destroy --stack-config-path aws-lakefs-advanced.yaml
+mlinfra terraform --action destroy --stack-config-path aws-lakefs.yaml
+mlinfra terraform --action destroy --stack-config-path aws-lakefs-advanced.yaml
 ```
 
 ## 📞 Support & Resources
@@ -250,10 +247,9 @@ Before you start, ensure you have the following:
    source venv/bin/activate
    ```
 
-2. **Install the Required Packages:**
-
+2. **Install the Required Packages:** 
    ```bash
-   pip install platinfra
+   pip install mlinfra
    ```
 
 ## Deployment Configuration
@@ -267,7 +263,7 @@ You can choose between two deployment configurations based on your needs:
 This configuration is suitable for a simple MLflow deployment with essential features. To deploy:
 
 ```bash
-platinfra terraform --action apply --stack-config-path <path-to-your-config>/aws-mlflow.yaml
+mlinfra terraform --action apply --stack-config-path examples/cloud_vm/mlflow/aws-mlflow.yaml
 ```
 
 ### 2. Advanced MLflow Deployment
@@ -277,7 +273,7 @@ platinfra terraform --action apply --stack-config-path <path-to-your-config>/aws
 This configuration includes additional features for a more robust MLflow deployment. To deploy:
 
 ```bash
-platinfra terraform --action apply --stack-config-path <path-to-your-config>/aws-mlflow-advanced.yaml
+mlinfra terraform --action apply --stack-config-path examples/cloud_vm/mlflow/aws-mlflow-advanced.yaml
 ```
 
 ## Configuration
@@ -321,36 +317,36 @@ Before deploying, ensure you have the following:
 
 ### 📦 Installation
 
-To install the `platinfra` package, you can use pip. It's recommended to create a Python virtual environment first:
+To install the `mlinfra` package, you can use pip. It's recommended to create a Python virtual environment first:
 
 ```bash
 python -m venv venv
 source venv/bin/activate  # On Windows use `venv\Scripts\activate`
-pip install platinfra
+pip install mlinfra
 ```
 
 ### 2. Basic Deployment
 
 To deploy using the basic configuration, run:
 
 ```bash
-platinfra terraform --action apply --stack-config-path aws-wand.yaml
+mlinfra terraform --action apply --stack-config-path examples/cloud_vm/wandb/aws-wandb.yaml
 ```
 
 ### 3. Advanced Deployment
 
 For an advanced setup with additional features, execute:
 
 ```bash
-platinfra terraform --action apply --stack-config-path aws-wand-advanced.yaml
+mlinfra terraform --action apply --stack-config-path examples/cloud_vm/wandb/aws-wandb-advanced.yaml
 ```
 
 ### 4. Verifying Deployment
 
 After applying the configurations, check the status of the Weights and Biases services:
 
 ```bash
-aws cloudformation describe-stacks --stack-name wand-stack
+aws cloudformation describe-stacks --stack-name aws-mlops-stack-mlflow
 ```
 
 ### 5. Accessing Weights and Biases UI
@@ -397,7 +393,7 @@ Before you start, ensure you have the following:
 
 2. **Install the Required Python Package:**
    ```bash
-   pip install platinfra
+   pip install mlinfra
    ```
 
 ## Deployment Configuration 📄
@@ -423,9 +419,9 @@ To deploy Prefect on AWS, follow these steps:
 2. **Deploy the Configuration:**
    Run the following command to apply the configuration:
    ```bash
-   platinfra terraform --action apply --stack-config-path <path-to-your-config-file>
+   mlinfra terraform --action apply --stack-config-path examples/cloud_vm/prefect/aws-prefect-advanced.yaml
    ```
-   Replace `<path-to-your-config-file>` with the path to either `aws-prefect.yaml` or `aws-prefect-advanced.yaml`.
+
 
 ## Conclusion 🎉
 
@@ -472,7 +468,7 @@ Before deploying Dagster on AWS, make sure you have:
     ```
 2. **Deploy the infrastructure:**
     ```bash
-    aws cloudformation deploy --template-file aws-dagster.yaml --stack-name DagsterBasicSetup
+    aws cloudformation deploy --template-file aws-dagster.yaml --stack-name aws-mlops-stack-dagster
     ```
 3. 🎉 **You're all set!** Access Dagster via the provided endpoint.
 
@@ -483,7 +479,7 @@ Before deploying Dagster on AWS, make sure you have:
     ```
 2. **Deploy the infrastructure:**
     ```bash
-    aws cloudformation deploy --template-file aws-dagster-advanced.yaml --stack-name DagsterAdvancedSetup
+    aws cloudformation deploy --template-file aws-dagster-advanced.yaml --stack-name aws-mlops-stack-dagster
     ```
 3. **Customize the parameters if needed:**
     Modify the `.yaml` file to suit your specific needs, including scaling options, security settings, and more.

docs/code/aws/kubernetes.md

@@ -1,5 +1,3 @@
-`kubernetes` deploys MLOps `stack` on top of Cloud provider's kubernetes. In case of AWS, its EKS.
-
 # 🐳 Kubernetes Examples
 
 Welcome to the **Kubernetes** examples! This section provides configurations and guides to help you deploy scalable MLOps stacks on Kubernetes clusters. Kubernetes is ideal for managing containerized applications, and these examples will help you harness its power for your machine learning workflows.
@@ -16,7 +14,8 @@ Before deploying the examples, make sure you have:
 ### 📦 Installation
 
 1. **Set Up Kubernetes Cluster**:
-    - If you don’t have a cluster, create one using your preferred platform. For example, using **minikube**:
+    - For cloud-based deployment, create a cluster using your preferred platform (e.g., EKS, GKE, AKS).
+    - For local development, you can use Minikube:
       ```bash
       minikube start
       ```
@@ -34,23 +33,19 @@ Before deploying the examples, make sure you have:
 
 4. **Deploy to Kubernetes**:
     Use `mlinfra` to deploy the chosen configuration:
-    ```bash
-    mlinfra terraform --action apply --stack-config-path ./config/k8s-config.yaml
-    ```
-
-## 📂 Available Examples
+ ```bash
+    mlinfra terraform --action apply --stack-config-path examples/kubernetes/lakefs/aws-lakefs.yaml
+  ```
 
-| Example File            | Description |
-| ------------------------| ----------- |
 ## 📂 Available Examples
 
 | Example File            | Description                                                   |
-| ------------------------| ------------------------------------------------------------- |
-| `Complete`              | Complete configuration for deploying ML projects on Kubernetes |
-| `LakeFS`                | Basic configuration for deploying LakeFS on Kubernetes       |
-| `LakeFS Advanced`       | Advanced configuration for LakeFS with additional cloud database storage options |
-| `MLflow`                | Configuration for deploying MLflow on Kubernetes             |
-| `Prefect`               | Configuration for deploying Prefect on Kubernetes            |
+| ----------------------- | ------------------------------------------------------------- |
+| `Complete`              | Comprehensive configuration for deploying ML projects on Kubernetes.       |
+| `LakeFS`                | Configuration for deploying LakeFS on Kubernetes.                  |
+| `MLflow`                | Configuration for deploying MLflow on Kubernetes.                      |
+| `Prefect`               | Configuration for deploying Prefect on Kubernetes.                      |
+
 
 
 ### ℹ️ Note:

docs/code/local/kind.md

@@ -1,5 +1,3 @@
-`kind` deploys MLOps `stack` on top of [kind cluster](https://kind.sigs.k8s.io/) on your machine. Please ensure that docker is installed and running on your machine, along with the latest version of kind.
-
 # Kind Deployment in Local Environment 🖥️
 
 This directory contains YAML configurations for deploying applications using **Kind** (Kubernetes IN Docker). Kind allows you to run Kubernetes clusters in Docker containers, making it an excellent tool for local development and testing.

docs/code/local/minikube.md

@@ -1,5 +1,3 @@
-`minikube` deploys MLOps `stack` on top of [minikube cluster](https://minikube.sigs.k8s.io/) on your machine. Please ensure that docker is installed and running on your machine, along with the latest version of minikube.
-
 # Minikube Deployment in Local Environment 🚀
 
 This directory contains YAML configurations for deploying applications using **Minikube**. Minikube is a tool that enables you to run Kubernetes clusters locally, making it an excellent choice for development and testing.