refactor: update titles and descriptions for clarity in Flyte documentation

Author: madeline-underwood

content/learning-paths/servers-and-cloud-computing/flyte-with-grpc/install-flyte.md

@@ -1,21 +1,22 @@
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-title: Install Flyte and Dependencies
+title: Install Flyte and gRPC tools on Axion
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+## Prepare the development environment
 
 In this section, you prepare a SUSE Linux Enterprise Server (SLES) arm64 virtual machine and install the core components required to build machine learning workflow pipelines using Flyte.
 
 Flyte provides workflow orchestration for scalable ML pipelines, while gRPC enables efficient communication between distributed services used within those pipelines.
 
-This environment ensures that the workflow orchestration tools and communication libraries run natively on Arm-based Axion processors.
+Running these tools natively on Arm-based Axion processors ensures efficient execution of ML workflows.
 
 ## Architecture overview
 
-This architecture represents a single-node development environment used to build and run distributed machine learning workflows.
+The development environment consists of a single-node setup used to build and run distributed machine learning workflows.
 
 ```text
 SUSE Linux Enterprise Server (arm64)

content/learning-paths/servers-and-cloud-computing/flyte-with-grpc/instance.md

@@ -1,12 +1,12 @@
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-title: Create a Google Axion C4A arm64 virtual machine on GCP
+title: Create a Google Axion C4A Arm virtual machine
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-## Provision a Google Axion C4A arm64 virtual machine
+## Provision Google Axion infrastructure
 
 In this section you'll create a Google Axion C4A arm64 virtual machine on Google Cloud Platform. You'll use the `c4a-standard-4` machine type, which provides 4 vCPUs and 16 GB of memory. This virtual machine hosts your Flyte ML Workflow with gRPC applications.
 

content/learning-paths/servers-and-cloud-computing/flyte-with-grpc/run-ml-pipeline.md

@@ -1,11 +1,13 @@
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-title: Run ML Training Pipeline
+title: Execute and validate the ML pipeline
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+## Run the distributed ML workflow
+
 In this section, you execute the distributed machine learning pipeline built using **Flyte and gRPC**.
 
 The ML workflow will:
@@ -107,7 +109,7 @@ Feature gRPC service running on port 50051
 Generating feature for: 20
 ```
 
-This confirms that the Flyte workflow successfully communicated with the gRPC service.
+The output confirms that the Flyte workflow successfully communicated with the gRPC service.
 
 ## What you've learned and what's next