Category: *Post Types

At re:Invent 2025, we introduce one new lens and two significant updates to the AWS Well-Architected Lenses specifically focused on AI workloads: the Responsible AI Lens, the Machine Learning (ML) Lens, and the Generative AI Lens. Together, these lenses provide comprehensive guidance for organizations at different stages of their AI journey, whether you’re just starting to experiment with machine learning or already deploying complex AI applications at scale.

We are delighted to announce an update to the AWS Well-Architected Generative AI Lens. This update features several new sections of the Well-Architected Generative AI Lens, including new best practices, advanced scenario guidance, and improved preambles on responsible AI, data architecture, and agentic workflows.

We are excited to announce the updated AWS Well-Architected Machine Learning Lens, now enhanced with the latest capabilities and best practices for building machine learning (ML) workloads on AWS.

Are you ready to maximize your Well-Architected and Cloud Optimization learning and networking time at re:Invent 2025? We have put together this comprehensive guide to help you plan your schedule and make the most of the Well-Architected and cloud optimization sessions available this year. These sessions will deliver the practical guidance your teams need to lead strategic cloud initiatives, design next-generation architectures, optimize costs, or secure AI-powered systems.

The global real-time payments market is experiencing significant growth. According to Fortune Business Insights, the market was valued at USD 24.91 billion in 2024 and is projected to grow to USD 284.49 billion by 2032, with a CAGR of 35.4%. Similarly, Grand View Research reports that the global mobile payment market, valued at USD 88.50 […]

Generative AI agents in production environments demand resilience strategies that go beyond traditional software patterns. AI agents make autonomous decisions, consume substantial computational resources, and interact with external systems in unpredictable ways. These characteristics create failure modes that conventional resilience approaches might not address. This post presents a framework for AI agent resilience risk analysis […]

In this post, we explore how Metagenomi built a scalable database and search solution for over 1 billion protein vectors using LanceDB and Amazon S3. The solution enables rapid enzyme discovery by transforming proteins into vector embeddings and implementing a serverless architecture that combines AWS Lambda, AWS Step Functions, and Amazon S3 for efficient nearest neighbor searches.

In this post, we explore how CommSec, Australia’s leading online broker, transitioned from a multicloud environment to AWS as their sole cloud provider while implementing Amazon Application Recovery Controller (ARC) zonal shift to maintain high availability and operational resilience. The consolidation resulted in significant benefits including 25% base capacity reduction, two times faster deployments, and improved failover capabilities through ARC zonal shift, enabling CommSec to continue serving millions of customers while meeting strict regulatory requirements.

This two-part series shows how Karrot developed a new feature platform, which consists of three main components: feature serving, a stream ingestion pipeline, and a batch ingestion pipeline. This post starts by presenting our motivation, our requirements, and the solution architecture, focusing on feature serving.

This two-part series shows how Karrot developed a new feature platform, which consists of three main components: feature serving, a stream ingestion pipeline, and a batch ingestion pipeline. This post covers the process of collecting features in real-time and batch ingestion into an online store, and the technical approaches for stable operation.