February 2025 monthly summary focusing on delivering scalable CPU-based ML inference on AWS Graviton and enhancing model-related documentation. Key achievements include updating DeepSeek-R1 Distill Model batch inference documentation in aws/aws-graviton-getting-started with a new ML blog link and delivering an end-to-end scalable inference workflow for Llama models on Graviton using Ray Serve in Kubernetes (deployment configurations, performance testing scripts, benchmarks, and a cost savings analysis) in awslabs/data-on-eks. No major bugs were reported this month. Business impact: improves scalability and cost-efficiency for CPU-based ML inference, enhances developer onboarding with clearer docs, and provides concrete performance benchmarks to guide future optimizations. Technologies demonstrated: AWS Graviton, Ray Serve, Kubernetes, Llama.cpp CPU inference, performance benchmarking, cost analysis, and documentation practices.

January 2025 monthly summary for aws/aws-graviton-getting-started. Key feature delivered: Documentation enhancement to support ML-on-Graviton workflows by adding a blog link for Small Language Models (SLMs) inference with llama.cpp on Graviton4 to the README. Commit: 503f2f077523ff85705bf06f89990566cd216f5e. Impact: Improved onboarding and discoverability of ML resources for Graviton4, reducing time-to-first-run for developers exploring ML on AWS Graviton. No major bugs fixed in this repository this month. Skills demonstrated: Git-based documentation updates, Markdown documentation best practices, alignment of external ML resources with project docs, understanding of Graviton4-based ML workflows.