Refactor documentation for Ray: standardize section titles and improve clarity

Author: madeline-underwood

content/learning-paths/servers-and-cloud-computing/ray-on-axion/_index.md

@@ -1,10 +1,6 @@
 ---
 title: Scale AI workloads with Ray on Google Cloud C4A Axion VM
 description: Deploy and run distributed AI workloads using Ray on Google Cloud Axion C4A Arm-based VMs, covering parallel tasks, hyperparameter tuning, and model serving with Ray Core, Train, Tune, and Serve.
-
-draft: true
-cascade:
-    draft: true
 
 minutes_to_complete: 30
 

content/learning-paths/servers-and-cloud-computing/ray-on-axion/distributed_workloads.md

@@ -32,7 +32,7 @@ results = ray.get([square.remote(i) for i in range(10)])
 print("Results:", results)
 ```
 
-### Explanation
+### Code explanation
 
 * `ray.init()` → connects to the running Ray cluster
 * `@ray.remote` → converts a function into a distributed task
@@ -92,7 +92,7 @@ trainer = TorchTrainer(
 trainer.fit()
 ```
 
-### Execute training
+### Run the training script
 
 ```bash
 python3 ray_train.py
@@ -115,14 +115,14 @@ The output is similar to:
 
 This confirms distributed training across multiple workers.
 
-## Explanation
+## Training code explanation
 
 * `TorchTrainer` → handles distributed training execution
 * `ScalingConfig(num_workers=2)` → runs training on 2 workers
 * Each worker executes training in parallel
 * Logs can appear from multiple processes
 
-## Ray Jobs View (Tasks & Training)
+## Ray Jobs view (tasks and training)
 
 ![Ray Dashboard Jobs tab showing successful execution of ray_test.py and ray_train.py#center](images/ray-jobs.png "Ray Jobs tab showing distributed tasks and training execution status")
 

content/learning-paths/servers-and-cloud-computing/ray-on-axion/setup_and_cluster.md

@@ -175,9 +175,9 @@ http://<VM-IP>:8265
 
 The Ray Dashboard provides visibility into jobs, tasks, and resource utilization.
 
-## Ray Dashboard Overview
+## Ray Dashboard overview
 
-![Ray Dashboard showing cluster overview, utilization, and navigation tabs#center](images/ray-dashboard.png "Ray Dashboard Overview showing cluster status and metrics")
+![Ray Dashboard showing cluster overview, utilization, and navigation tabs#center](images/ray-dashboard.png "Ray Dashboard overview showing cluster status and metrics")
 
 The Ray Dashboard helps monitor distributed execution and debug workloads in real time.
 

content/learning-paths/servers-and-cloud-computing/ray-on-axion/tuning_serving_benchmark.md

@@ -42,14 +42,14 @@ results = tuner.fit()
 print("Best result:", results.get_best_result(metric="score", mode="max"))
 ```
 
-### Explanation
+### Code explanation
 
 * `tune.grid_search()` → tries multiple hyperparameter values
 * Each value runs as a separate parallel trial
 * `session.report()` → sends metrics back to Ray
 * `Tuner.fit()` → executes all trials
 
-### Execute tuning
+### Run hyperparameter tuning
 
 ```bash
 python3 ray_tune.py
@@ -126,7 +126,7 @@ app = Model.bind()
 serve.run(app)
 ```
 
-### Explanation
+### Code explanation
 
 * `serve.start()` → initializes serving system
 * `@serve.deployment` → defines deployable service
@@ -150,7 +150,7 @@ The output is similar to:
 {"message":"Hello from Ray Serve on Arm VM!"}
 ```
 
-## Ray Tune Execution in Dashboard
+## Ray Tune execution in Ray Dashboard
 
 ![Ray Dashboard Jobs tab showing ray_tune.py trials with SUCCEEDED status#center](images/ray-jobs-status.png "Ray Tune trials executed successfully with different configurations")
 
@@ -183,7 +183,7 @@ print("Execution Time:", end - start)
 ```
 
 
-### Execute benchmark
+### Run the benchmark
 
 ```bash
 ray stop