refactor: update titles and descriptions for clarity in Flyte and gRPC documentation

Author: madeline-underwood

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

@@ -2,10 +2,6 @@
 title: Build ML Workflow Pipelines with Flyte and gRPC on Google Cloud C4A Axion processors
 description: Learn how to build scalable machine learning workflow pipelines on Google Cloud C4A Axion processors using Flyte for workflow orchestration and gRPC for distributed service communication.
 
-draft: true
-cascade:
-    draft: true
-    
 minutes_to_complete: 30
 
 who_is_this_for: This is an introductory topic for developers, data engineers, and ML engineers who want to build scalable machine learning workflow pipelines on Arm64-based Google Cloud C4A Axion processors using Flyte workflow orchestration and gRPC-based microservices.

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

@@ -1,14 +1,16 @@
 ---
-title: ML Pipeline Architecture
+title: Understand the distributed ML architecture
 weight: 8
 
 ### FIXED, DO NOT MODIFY
 layout: learningpathall
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+## Analyze the ML pipeline architecture
+
 In this section, you explore the architecture behind the distributed machine learning pipeline built using Flyte and gRPC on Google Axion Arm-based infrastructure.
 
-This architecture demonstrates how modern ML workflows are orchestrated using workflow engines while delegating specific tasks to distributed services.
+The architecture demonstrates how modern ML workflows are orchestrated using workflow engines while delegating specific tasks to distributed services.
 
 Flyte manages the pipeline orchestration, while gRPC enables efficient communication between workflow tasks and external services.
 
@@ -75,7 +77,7 @@ Typical preprocessing steps include:
 ### Feature engineering service (gRPC)
 Feature engineering is implemented as a gRPC microservice.
 
-This design allows feature-generation logic to run independently of the workflow engine.
+The design allows feature-generation logic to run independently of the workflow engine.
 
 Benefits include:
 

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

@@ -1,10 +1,14 @@
 ---
-title: Get started with Flyte ML Workflow Pipelines with gRPC on Google Axion C4A
+title: Understand Flyte and gRPC ML workflows on Google Axion
 weight: 2
 
 layout: "learningpathall"
 ---
 
+## Why Flyte and gRPC on Google Axion C4A
+
+Flyte is an open-source workflow orchestration platform used to build scalable and reproducible data and machine learning pipelines. Combined with gRPC for efficient distributed service communication, Flyte enables developers to define workflows as Python tasks while delegating specific operations to independent microservices. Running this stack on Google Axion C4A Arm-based processors provides efficient, scalable infrastructure for executing modern ML workflows and distributed data processing tasks.
+
 ## Explore Axion C4A Arm instances in Google Cloud
 
 Google Axion C4A is a family of Arm-based virtual machines built on Google’s custom Axion CPU, which is based on Arm Neoverse-V2 cores. Designed for high-performance and energy-efficient computing, these virtual machines offer strong performance for data-intensive and analytics workloads such as big data processing, in-memory analytics, columnar data processing, and high-throughput data services.
@@ -15,9 +19,9 @@ These characteristics make Axion C4A instances well-suited for modern analytics
 
 To learn more, see the Google blog [Introducing Google Axion Processors, our new Arm-based CPUs](https://cloud.google.com/blog/products/compute/introducing-googles-new-arm-based-cpu).
 
-## Explore Flyte ML Workflow Pipelines with gRPC on Google Axion C4A (Arm Neoverse V2)
+## Flyte and gRPC for distributed ML workflows
 
-Flyte is an open-source workflow orchestration platform used to build scalable and reproducible data and machine learning pipelines. It allows developers to define workflows as Python tasks, simplifying the management of complex ML processes such as data preparation, feature engineering, and model training. 
+Flyte allows developers to define workflows as Python tasks, simplifying the management of complex ML processes such as data preparation, feature engineering, and model training. 
 
 gRPC enables fast communication between distributed services within these pipelines. Running Flyte with gRPC on Google Axion C4A Arm-based processors provides efficient, scalable infrastructure for executing modern ML workflows and distributed data processing tasks.
 

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

@@ -1,16 +1,18 @@
 ---
-title: Create Feature Engineering gRPC Service
+title: Build a gRPC feature engineering service
 weight: 5
 
 ### FIXED, DO NOT MODIFY
 layout: learningpathall
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+## Create distributed feature engineering with gRPC
+
 In modern machine learning pipelines, feature engineering is often implemented as a separate service so it can scale independently from the training workflow.
 
 In this section, you create a **gRPC-based feature engineering service** that generates features used by the machine learning pipeline.
 
-This service will later be called by the Flyte workflow during pipeline execution.
+The Flyte workflow will call this service during pipeline execution.
 
 ## Architecture overview
 
@@ -79,7 +81,7 @@ python -m grpc_tools.protoc \
 feature.proto
 ```
 
-This generates the following files:
+The command generates the following files:
 
 ```text
 feature_pb2.py
@@ -143,7 +145,7 @@ if __name__ == "__main__":
     serve()
 ```
 
-This service receives a value from the workflow and generates a derived feature used during model training.
+The service receives a value from the workflow and generates a derived feature used during model training.
 
 ## Run the feature service