Add scaling resources

docs/getting_started/scaling_resources.md

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+---
+title: Autoscaling Resources
+---
+
+Autoscaling allows organizations to dynamically manage compute resources based on demand, optimizing efficiency and cost. 
+
+When running machine learning experiments or large-scale compute tasks, demand for resources fluctuates. Autoscaling ensures that:
+- **Resources are available when needed**, preventing delays in task execution.
+- **Idle resources are automatically spun down**, reducing unnecessary costs.
+- **Workloads can be distributed efficiently**.
+
+ClearML offers the following resource autoscaling solutions:
+* Built-in GUI applications - Built-in applications to autoscale, no code required (available under the Pro and Enterprise plans)
+  * AWS Autoscaler
+  * GCP Autoscaler
+* Kubernetes autoscaling
+* Custom autoscaler implementation using the `AutoScaler` class
+
+### GUI Autoscaler Applications
+For users on **Pro** and **Enterprise** plans, ClearML provides a UI applications to configure autoscaling for cloud 
+resources. These applications include:
+* [**AWS Autoscaler**](../webapp/applications/apps_aws_autoscaler.md): Automatically provisions and shuts down AWS EC2 instances based on workload demand.
+* [**GCP Autoscaler**](../webapp/applications/apps_gcp_autoscaler.md): Manages Google Cloud instances dynamically according to defined budgets.
+
+These applications allow users to set up autoscaling with minimal configuration, defining compute budgets and resource limits directly through the UI.
+
+### Kubernetes Autoscaling
+ClearML integrates with **Kubernetes**, allowing agents to be deployed within a cluster. Kubernetes handles:
+- Automatic pod creation for executing tasks.
+- Resource allocation and scaling based on workload.
+- Optional integration with Kubernetes' **Cluster Autoscaler**, which adjusts the number of nodes dynamically.
+
+This is particularly useful for organizations already using Kubernetes for workload orchestration.
+
+### Custom Autoscaler Implementation
+Users can build their own autoscaler using the `clearml.automation.auto_scaler.AutoScaler` class which enables:
+* Direct control over instance scaling logic.
+* Custom rules for resource allocation.
+* Budget-conscious decision-making based on predefined policies.
+
+This method requires some scripting.
+
+$$$$$See the AWS Autoscaler Example to see the `AutoScaler` class in action. This script can be adjusted to scale GCP resources 
+as well
+
+demonstrates how to use the clearml.automation.auto_scaler module to implement a service that optimizes AWS EC2 instance scaling according to a defined instance budget