Merge pull request #3185 from madeline-underwood/serv2

jasonrandrews · web-flow · commit b5665fc60380 · 2026-04-21T16:48:27.000-05:00
Add descriptions to various learning paths for improved clarity and c…
diff --git a/content/learning-paths/servers-and-cloud-computing/intro/_index.md b/content/learning-paths/servers-and-cloud-computing/intro/_index.md
@@ -1,13 +1,14 @@
 ---
 title: Get started with Servers and Cloud Computing
+description: Learn where Arm architecture is used in servers and cloud computing, and find Arm-based hardware platforms for software development.
 
 minutes_to_complete: 10
 
 who_is_this_for: This is an introductory topic for software developers working on server and cloud applications who are new to the Arm architecture.
 
 learning_objectives:
-    - Understand where the Arm architecture is used in servers and cloud computing
-    - Find server and cloud hardware to use for software development
+    - Identify where Arm architecture is used in servers and cloud computing
+    - Locate server and cloud hardware for software development
 
 prerequisites:
     - None
diff --git a/content/learning-paths/servers-and-cloud-computing/irq-tuning-guide/_index.md b/content/learning-paths/servers-and-cloud-computing/irq-tuning-guide/_index.md
@@ -1,5 +1,6 @@
 ---
 title: Optimize network interrupt handling on Arm servers
+description: Analyze and optimize interrupt request (IRQ) patterns on Arm Linux servers to improve network workload performance through IRQ distribution strategies.
 
    
 minutes_to_complete: 20
diff --git a/content/learning-paths/servers-and-cloud-computing/java-gc-tuning/_index.md b/content/learning-paths/servers-and-cloud-computing/java-gc-tuning/_index.md
@@ -5,10 +5,12 @@ minutes_to_complete: 45
 
 who_is_this_for: This Learning Path is for Java developers aiming to optimize application performance on Arm-based servers, especially those migrating applications from x86-based to Arm-based instances. 
 
+description: Monitor, interpret, and optimize Java Garbage Collector performance on Arm servers by comparing different GCs and tuning parameters for your workload.
+
 learning_objectives: 
-    - Describe the key differences between individual Java Garbage Collectors (GCs).
-    - Monitor and interpret Garbage Collector performance metrics.
-    - Adjust core parameters to optimize performance for your specific workload.
+    - Describe the key differences between individual Java Garbage Collectors (GCs)
+    - Monitor and interpret Garbage Collector performance metrics
+    - Adjust core parameters to optimize performance for your specific workload
 
 prerequisites:
     - An Arm-based instance from a cloud service provider, or an on-premise Arm server.
diff --git a/content/learning-paths/servers-and-cloud-computing/java-on-axion/_index.md b/content/learning-paths/servers-and-cloud-computing/java-on-axion/_index.md
@@ -3,8 +3,8 @@ title: Run Java applications on Google Axion processors
 
 minutes_to_complete: 20
 
+description: Deploy and optimize Java applications on Google Cloud Axion processors by testing JDK versions and performance optimization flags.
 who_is_this_for: This is an introductory topic for software developers who want to learn how to run their Java-based applications on Arm-based Google Axion processors in Google Cloud. Most Java applications will run on Axion with no changes needed, but there are optimizations that can help improve application performance on Axion.
-
 learning_objectives: 
     - Create an Arm-based VM instance with Google Axion CPU
     - Deploy a Java application on Axion
diff --git a/content/learning-paths/servers-and-cloud-computing/java-on-azure/_index.md b/content/learning-paths/servers-and-cloud-computing/java-on-azure/_index.md
@@ -3,6 +3,7 @@ title: Deploy Java applications on Azure Cobalt 100 processors
 
 minutes_to_complete: 30   
 
+description: Deploy Java on Azure Cobalt 100 Arm virtual machines and benchmark application performance with JMH microbenchmarks.
 who_is_this_for: This is an introductory topic about Java deployment and benchmarking on Microsoft Azure Cobalt 100 Arm-based virtual machines. It is designed for developers migrating Java applications from x86_64 to Arm architecture.
 
 learning_objectives: 
diff --git a/content/learning-paths/servers-and-cloud-computing/java-perf-flamegraph/_index.md b/content/learning-paths/servers-and-cloud-computing/java-perf-flamegraph/_index.md
@@ -4,6 +4,8 @@ minutes_to_complete: 30
 
 who_is_this_for: This is an introductory topic for developers who want to analyze the performance of Java applications on Arm Neoverse-based servers using flame graphs.
 
+description: Profile Java applications on Arm Neoverse servers using flame graphs generated with async-profiler and Java agents to identify performance bottlenecks.
+
 learning_objectives: 
   - Set up a benchmarking environment using Tomcat and wrk2
   - Generate flame graphs using async-profiler
diff --git a/content/learning-paths/servers-and-cloud-computing/jenkins/_index.md b/content/learning-paths/servers-and-cloud-computing/jenkins/_index.md
@@ -5,6 +5,8 @@ minutes_to_complete: 30
 
 who_is_this_for: This Learning Path is for software developers deploying and optimizing Jenkins workloads on Arm Linux environments, specifically on Microsoft Azure Cobalt 100 processors and Google Cloud C4A virtual machines powered by Axion processors.
 
+description: Deploy Jenkins on Azure Cobalt 100 and Google Axion virtual machines, validate installation, and execute Arm-native CI/CD pipelines including Docker workflows.
+
 learning_objectives: 
     - Provision an Azure Arm64 virtual machine using the Azure console with Ubuntu Pro 24.04 LTS
     - Provision an Arm-based SUSE Linux virtual machine on Google Cloud (C4A with Axion processors)
diff --git a/content/learning-paths/servers-and-cloud-computing/kafka-azure/_index.md b/content/learning-paths/servers-and-cloud-computing/kafka-azure/_index.md
@@ -5,6 +5,8 @@ minutes_to_complete: 30
 
 who_is_this_for: This is an advanced topic for developers looking to migrate their Apache Kafka workloads from x86_64 to Arm-based platforms, specifically on Microsoft Azure Cobalt 100 (arm64) virtual machines.
 
+description: Deploy Apache Kafka on Azure Cobalt 100 Arm virtual machines and benchmark message throughput performance.
+
 learning_objectives: 
     - Provision an Azure Arm64 virtual machine using Azure console, with Ubuntu Pro 24.04 LTS as the base image
     - Deploy Kafka on an Ubuntu virtual machine
diff --git a/content/learning-paths/servers-and-cloud-computing/kafka/_index.md b/content/learning-paths/servers-and-cloud-computing/kafka/_index.md
@@ -5,6 +5,8 @@ minutes_to_complete: 90
 
 who_is_this_for: This is an advanced topic for software developers who want to learn how to use Kafka and Zookeeper.
 
+description: Deploy and configure a Kafka cluster with Zookeeper on Arm servers, test event streaming, and automate deployment on AWS and Google Cloud.
+
 learning_objectives:
     - Install Zookeeper and Kafka
     - Configure Zookeeper to work with Kafka
diff --git a/content/learning-paths/servers-and-cloud-computing/kedify-http-autoscaling/_index.md b/content/learning-paths/servers-and-cloud-computing/kedify-http-autoscaling/_index.md
@@ -5,6 +5,8 @@ minutes_to_complete: 45
 
 who_is_this_for: This is an introductory topic for developers running HTTP workloads on Kubernetes who want to enable event-driven autoscaling with KEDA and Kedify.
 
+description: Enable event-driven autoscaling for HTTP workloads on Kubernetes by installing Kedify and KEDA with Helm and testing autoscaling behavior.
+
 learning_objectives:
   - Install Kedify (KEDA build, HTTP Scaler, and Kedify Agent) with Helm
   - Verify that Kedify and KEDA components are running in the cluster
diff --git a/content/learning-paths/servers-and-cloud-computing/keras-core/_index.md b/content/learning-paths/servers-and-cloud-computing/keras-core/_index.md
@@ -5,6 +5,8 @@ minutes_to_complete: 30
 
 who_is_this_for: This is an introductory topic for engineers who want to create a neural network model on Arm machines.
 
+description: Create, train, and evaluate a neural network model on Arm servers using Keras Core with TensorFlow, PyTorch, and JAX backends.
+
 learning_objectives: 
     - Create a simple neural network model using Keras Core
     - Train and evaluate your neural network model with different backends
diff --git a/content/learning-paths/servers-and-cloud-computing/kernel-build/_index.md b/content/learning-paths/servers-and-cloud-computing/kernel-build/_index.md
@@ -5,6 +5,8 @@ minutes_to_complete: 60
 
 who_is_this_for: This is an advanced topic for software developers building custom Linux kernels on Arm servers and cloud instances.
 
+description: Compile and install custom Linux kernels on Arm cloud instances using TuxMake with configurations for 64 KB page sizes and Fastpath testing.
+
 learning_objectives:
     - Set up a build environment for compiling Linux kernels on Arm cloud instances
     - Build custom Linux kernels with various configurations using TuxMake
diff --git a/content/learning-paths/servers-and-cloud-computing/kubearchinspect/_index.md b/content/learning-paths/servers-and-cloud-computing/kubearchinspect/_index.md
@@ -5,11 +5,13 @@ minutes_to_complete: 15
 
 who_is_this_for: This is an introductory topic for software developers who want to ensure containers running in a Kubernetes cluster support the Arm architecture.
 
+description: Identify and migrate container images in a Kubernetes cluster to Arm-compatible versions using KubeArchInspect reports.
+
 learning_objectives: 
-    - Run KubeArchInspect to generate a report on the containers running in a Kubernetes cluster.
-    - Discover which images support the Arm architecture.
-    - Understand common reasons for an image not supporting Arm.
-    - Make configuration changes to upgrade images with Arm support.
+    - Run KubeArchInspect to generate a report on the containers running in a Kubernetes cluster
+    - Discover which images support the Arm architecture
+    - Understand common reasons for an image not supporting Arm
+    - Make configuration changes to upgrade images with Arm support
 
 prerequisites:
     - A running Kubernetes cluster accessible with `kubectl`.
diff --git a/content/learning-paths/servers-and-cloud-computing/lambda_functions/_index.md b/content/learning-paths/servers-and-cloud-computing/lambda_functions/_index.md
@@ -3,7 +3,9 @@ title: Learn how to deploy AWS Lambda functions
 
 minutes_to_complete: 30   
 
-who_is_this_for: This is an introductory topic for software developers who want to learn how to deploy Lambda functions on AWS Graviton processors. 
+who_is_this_for: This is an introductory topic for software developers who want to learn how to deploy Lambda functions on AWS Graviton processors.
+
+description: Deploy AWS Lambda functions on Graviton processors using Terraform for Python and Node.js runtimes.
 
 learning_objectives: 
     - Deploy Lambda functions on Graviton processors using Terraform
diff --git a/content/learning-paths/servers-and-cloud-computing/libhugetlbfs/_index.md b/content/learning-paths/servers-and-cloud-computing/libhugetlbfs/_index.md
@@ -5,9 +5,11 @@ minutes_to_complete: 60
 
 who_is_this_for: This is an advanced topic for engineers looking for ways to increase performance on Arm servers.
 
+description: Enable and measure libhugetlbfs performance improvements for MySQL and other workloads on Arm Linux servers.
+
 learning_objectives:
     - Enable libhugetlbfs on an Arm server running Linux
-    - Evaluate performance improvements for workloads such as MySQL.
+    - Evaluate performance improvements for workloads such as MySQL
 
 prerequisites:
     - An Arm server or virtual machine instance from a cloud service provider with Ubuntu installed
diff --git a/content/learning-paths/servers-and-cloud-computing/llama-vision/_index.md b/content/learning-paths/servers-and-cloud-computing/llama-vision/_index.md
@@ -5,10 +5,12 @@ minutes_to_complete: 45
 
 who_is_this_for: This Learning Path is for software developers and ML engineers who are interested in deploying a production-ready vision chatbot for their application with optimized performance on the Arm Architecture.
 
+description: Build a production-ready vision chatbot on Google Axion using Streamlit, PyTorch, and Hugging Face Transformers with a quantized Llama 3.2-Vision model.
+
 learning_objectives:
-    - Build a frontend with Streamlit to input images and prompts.
-    - Build the backend to download a Llama 3.2-Vision model, quantize it, and run it using PyTorch and Hugging Face Transformers.
-    - Monitor and analyze inference on Arm CPUs.
+    - Build a frontend with Streamlit to input images and prompts
+    - Build the backend to download a Llama 3.2-Vision model, quantize it, and run it using PyTorch and Hugging Face Transformers
+    - Monitor and analyze inference on Arm CPUs
 
 prerequisites:
     - A Google Cloud Axion compute instance or [any Arm-based instance](/learning-paths/servers-and-cloud-computing/csp/) from a cloud service provider with at least 32 cores.
diff --git a/content/learning-paths/servers-and-cloud-computing/llama_cpp_streamline/_index.md b/content/learning-paths/servers-and-cloud-computing/llama_cpp_streamline/_index.md
@@ -3,7 +3,9 @@ title: Profile llama.cpp performance with Arm Streamline and KleidiAI LLM kernel
 
 minutes_to_complete: 60
 
-who_is_this_for: This is an advanced topic for software developers, performance engineers, and AI practitioners who want to optimize llama.cpp performance on Arm-based CPUs. 
+who_is_this_for: This is an advanced topic for software developers, performance engineers, and AI practitioners who want to optimize llama.cpp performance on Arm-based CPUs.
+
+description: Optimize llama.cpp on Arm CPUs by integrating Streamline Annotations to profile Prefill and Decode stages, analyze operators, and evaluate multi-core execution.
 
 learning_objectives:
     - Profile llama.cpp architecture and identify the role of the Prefill and Decode stages
diff --git a/content/learning-paths/servers-and-cloud-computing/lse/_index.md b/content/learning-paths/servers-and-cloud-computing/lse/_index.md
@@ -5,6 +5,8 @@ minutes_to_complete: 30
 
 who_is_this_for: This is an introductory topic for software developers who want to learn about Large System Extensions and use them in an application.
 
+description: Understand Large System Extensions (LSE) for Arm processors and verify whether applications use LSE for improved atomic operation performance.
+
 learning_objectives:
     - Learn about Large System Extensions
     - Find out if an application uses Large System Extensions
diff --git a/content/learning-paths/servers-and-cloud-computing/mariadb/_index.md b/content/learning-paths/servers-and-cloud-computing/mariadb/_index.md
@@ -5,6 +5,8 @@ minutes_to_complete: 90
 
 who_is_this_for: This is an introductory topic for software developers who want to deploy MariaDB on Arm servers.
 
+description: Deploy MariaDB on Arm cloud instances across AWS, Azure, and Google Cloud using Docker, Amazon RDS, and automation with Terraform and Ansible.
+
 learning_objectives: 
     - Deploy MariaDB on virtual machines from different cloud service providers 
     - Deploy MariaDB using Docker
diff --git a/content/learning-paths/servers-and-cloud-computing/memcached/_index.md b/content/learning-paths/servers-and-cloud-computing/memcached/_index.md
@@ -5,6 +5,7 @@ minutes_to_complete: 10
 
 who_is_this_for: This is an introductory topic for developers who want to use memcached as their in-memory key-value store.
 
+description: Install memcached on Arm cloud servers and benchmark in-memory key-value store performance using open-source tools.
 
 learning_objectives:
 - Install and run memcached on your Arm-based cloud server
diff --git a/content/learning-paths/servers-and-cloud-computing/memory_consistency/_index.md b/content/learning-paths/servers-and-cloud-computing/memory_consistency/_index.md
@@ -5,10 +5,12 @@ minutes_to_complete: 150
 
 who_is_this_for: This is an advanced topic for developers seeking practical ways to test thread synchronization approaches in the Arm memory model.
 
+description: Test and validate thread synchronization approaches in the Arm memory model using Herd7, Litmus7, and Arm hardware with assembly snippets.
+
 learning_objectives:
-    - Test thread synchronization assembly snippets against the formal definition of the Arm memory model.
-    - Test thread synchronization assembly snippets on Arm hardware.
-    - Compare the results of different thread synchronization approaches. 
+    - Test thread synchronization assembly snippets against the formal definition of the Arm memory model
+    - Test thread synchronization assembly snippets on Arm hardware
+    - Compare the results of different thread synchronization approaches 
 
 prerequisites:
     - An understanding of memory consistency models (such as Sequential Consistency, Weak Ordering, Relaxed Consistency, and Processor Consistency).
diff --git a/content/learning-paths/servers-and-cloud-computing/migrate-ease/_index.md b/content/learning-paths/servers-and-cloud-computing/migrate-ease/_index.md
@@ -6,11 +6,12 @@ minutes_to_complete: 45
 
 who_is_this_for: This is an introductory topic for developers looking to migrate applications to Arm-based servers using migrate-ease, a code analysis tool that scans source code repositories to identify architecture-specific porting issues before migration.
 
+description: Scan source code for architecture-specific portability issues using migrate-ease to identify and resolve AArch64 porting challenges before migration.
 
 learning_objectives:
-    - Identify architecture-specific dependencies in your application's source code.
-    - Recognize common migration challenges and how to resolve them.
-    - Use migrate-ease to detect and address AArch64 portability issues.
+    - Identify architecture-specific dependencies in your application's source code
+    - Recognize common migration challenges and how to resolve them
+    - Use migrate-ease to detect and address AArch64 portability issues
 
 prerequisites:
     - Access to an [Arm-based instance](/learning-paths/servers-and-cloud-computing/csp/) for testing and validation.
diff --git a/content/learning-paths/servers-and-cloud-computing/migration/_index.md b/content/learning-paths/servers-and-cloud-computing/migration/_index.md
@@ -5,6 +5,8 @@ minutes_to_complete: 15
 
 who_is_this_for: This is an introductory topic for software developers looking to migrate applications to Arm servers.
 
+description: Set up an Arm development environment, analyze dependencies, and understand common challenges and scenarios for migrating applications to Arm servers.
+
 learning_objectives:
     - Set up an Arm development machine
     - Analyze application dependencies
diff --git a/content/learning-paths/servers-and-cloud-computing/milvus-rag/_index.md b/content/learning-paths/servers-and-cloud-computing/milvus-rag/_index.md
@@ -5,9 +5,11 @@ minutes_to_complete: 20
 
 who_is_this_for: This is an introductory topic for software developers who want to create a Retrieval-Augmented Generation (RAG) application on Arm servers.
 
+description: Build a Retrieval-Augmented Generation (RAG) application on Arm servers using Zilliz Cloud for vector search and llama.cpp for LLM inference.
+
 learning_objectives: 
-    - Create a simple RAG application using Zilliz Cloud.
-    - Launch an LLM service on Arm servers.
+    - Create a simple RAG application using Zilliz Cloud
+    - Launch an LLM service on Arm servers
 
 prerequisites:
     - A basic understanding of a RAG pipeline.
diff --git a/content/learning-paths/servers-and-cloud-computing/ml-perf/_index.md b/content/learning-paths/servers-and-cloud-computing/ml-perf/_index.md
@@ -3,14 +3,13 @@ title: Measure Machine Learning Inference Performance on Arm servers
 
 minutes_to_complete: 20
 
-who_is_this_for: This is an introductory topic for software developers interested
-  in benchmarking machine learning workloads on Arm servers.
+who_is_this_for: This is an introductory topic for software developers interested in benchmarking machine learning workloads on Arm servers.
 
+description: Benchmark machine learning inference performance on Arm servers using TensorFlow and the MLPerf Inference benchmark suite from MLCommons.
 
 learning_objectives:
-- Install and run TensorFlow on your Arm-based cloud server.
-- Use MLPerf Inference benchmark suite, an open-sourced benchmark from MLCommons to
-  test ML performance on your Arm server.
+- Install and run TensorFlow on your Arm-based cloud server
+- Use MLPerf Inference benchmark suite, an open-sourced benchmark from MLCommons to test ML performance on your Arm server
 
 prerequisites:
 - An [Arm based instance](/learning-paths/servers-and-cloud-computing/csp/) from an appropriate
diff --git a/content/learning-paths/servers-and-cloud-computing/mongodb-on-azure/_index.md b/content/learning-paths/servers-and-cloud-computing/mongodb-on-azure/_index.md
@@ -5,6 +5,8 @@ minutes_to_complete: 30
 
 who_is_this_for: This is an introductory topic for software developers who want to migrate MongoDB workloads to Arm-based platforms, with a focus on Microsoft Azure Cobalt 100 Arm64 instances.
 
+description: Deploy MongoDB on Azure Cobalt 100 Arm virtual machines and benchmark database performance using mongotop and mongostat monitoring tools.
+
 learning_objectives: 
     - Provision an Arm64-based Cobalt 100 virtual machine in Azure using Ubuntu Pro 24.04 LTS
     - Deploy MongoDB on the Cobalt 100 instance
diff --git a/content/learning-paths/servers-and-cloud-computing/mongodb-on-gcp/_index.md b/content/learning-paths/servers-and-cloud-computing/mongodb-on-gcp/_index.md
@@ -5,6 +5,8 @@ minutes_to_complete: 15
 
 who_is_this_for: This introductory topic is for software developers who want to migrate MongoDB workloads from x86_64 to Arm-based platforms, specifically on Google Axion-based C4A virtual machines.
 
+description: Deploy MongoDB on Google Cloud Axion C4A virtual machines and benchmark database performance with Yahoo Cloud Serving Benchmark (YCSB).
+
 learning_objectives:
   - Create an Arm virtual machine on Google Cloud (C4A Axion family)
   - Install and run MongoDB on the Arm-based C4A instance
diff --git a/content/learning-paths/servers-and-cloud-computing/mongodb/_index.md b/content/learning-paths/servers-and-cloud-computing/mongodb/_index.md
@@ -5,10 +5,14 @@ author: Pareena Verma
 
 minutes_to_complete: 30
 
+who_is_this_for: This is an introductory topic for software developers who want to learn how to deploy and measure MongoDB performance on Arm servers.
+
+description: Install MongoDB on Arm servers and benchmark database performance using Yahoo Cloud Serving Benchmark (YCSB) to compare against other architectures.
+
 learning_objectives:
-- Install and run MongoDB on an Arm server.
-- Test MongoDB performance using open-source tooling.
-- Measure and compare the performance of MongoDB on Arm versus other architectures with Yahoo Cloud Serving Benchmark (YCSB). 
+- Install and run MongoDB on an Arm server
+- Test MongoDB performance using open-source tooling
+- Measure and compare the performance of MongoDB on Arm versus other architectures with Yahoo Cloud Serving Benchmark (YCSB)
 
 prerequisites:
 - An [Arm based instance](/learning-paths/servers-and-cloud-computing/csp/) from a cloud service provider.
diff --git a/content/learning-paths/servers-and-cloud-computing/mpi/_index.md b/content/learning-paths/servers-and-cloud-computing/mpi/_index.md
@@ -5,6 +5,8 @@ minutes_to_complete: 30
 
 who_is_this_for: This is an advanced topic for HPC software developers writing MPI applications.
 
+description: Debug, profile, and optimize MPI parallel applications on Arm servers using Linaro Forge, gdb, and Arm Performance Libraries.
+
 learning_objectives: 
     - Debug and fix a parallel application
     - Profile and optimize your code
diff --git a/content/learning-paths/servers-and-cloud-computing/multi-accuracy-libamath/_index.md b/content/learning-paths/servers-and-cloud-computing/multi-accuracy-libamath/_index.md
@@ -4,7 +4,9 @@ title: Control floating-point accuracy modes in Arm Performance Libraries
 minutes_to_complete: 20
 author: Joana Cruz
 
-who_is_this_for: This is an introductory topic for developers who want to use the different accuracy modes for vectorized math functions in Libamath, a component of Arm Performance Libraries. 
+who_is_this_for: This is an introductory topic for developers who want to use the different accuracy modes for vectorized math functions in Libamath, a component of Arm Performance Libraries.
+
+description: Select and apply accuracy modes for vectorized math functions in Libamath to balance performance and precision for your application.
 
 learning_objectives: 
     - Describe how accuracy is defined and measured in Libamath
