Category: Learning Levels

In this post, we share how Convera used Amazon Verified Permissions to build a fine-grained authorization model for their API platform.

In this post, we explore how the Amazon Key team used Amazon EventBridge to modernize their architecture, transforming a tightly coupled monolithic system into a resilient, event-driven solution. We explore the technical challenges we faced, our implementation approach, and the architectural patterns that helped us achieve improved reliability and scalability. The post covers our solutions for managing event schemas at scale, handling multiple service integrations efficiently, and building an extensible architecture that accommodates future growth.

This post explores the architectural patterns, challenges, and best practices for building cross-partition failover, covering network connectivity, authentication, and governance. By understanding these constraints, you can design resilient cloud-native applications that balance regulatory compliance with operational continuity.

In this post, we demonstrate how to utilize AWS Network Firewall to secure an Amazon EVS environment, using a centralized inspection architecture across an EVS cluster, VPCs, on-premises data centers and the internet. We walk through the implementation steps to deploy this architecture using AWS Network Firewall and AWS Transit Gateway.

In this post, we’ll explore a reference architecture that helps enterprises govern their Amazon Bedrock implementations using Amazon API Gateway. This pattern enables key capabilities like authorization controls, usage quotas, and real-time response streaming. We’ll examine the architecture, provide deployment steps, and discuss potential enhancements to help you implement AI governance at scale.

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 an efficient approach to managing encryption keys in a multi-tenant SaaS environment through centralization, addressing challenges like key proliferation, rising costs, and operational complexity across multiple AWS accounts and services. We demonstrate how implementing a centralized key management strategy using a single AWS KMS key per tenant can maintain security and compliance while reducing operational overhead as organizations scale.

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.