Category: Learning Levels

Introduction Continuous improvement depends on experimentation. Teams know that the fastest path to better outcomes is to test changes against real user behavior, measure results, and iterate. In practice, sustaining that cycle is slow and costly because the overhead compounds with each attempt. Three barriers slow teams down: 1. Planning cost — Turning a proposed […]

AWS Health Planned Lifecycle Events signal when a managed service version is nearing end of standard support. Learn how to automate these upgrades end to end with AWS DevOps Agent and Kiro: detect the event, investigate the upgrade path, apply validated code changes, and open a pull request for human review.

Introduction Large-scale machine learning workloads: training, fine-tuning, and inference run on clusters of hundreds to thousands of GPU instances for days or weeks at a stretch. Keeping operational visibility across a fleet of this size is a constant challenge: hardware health events, node lifecycle transitions, capacity fluctuations, and workload-level issues appear in the event stream around […]

Walk through a complete CloudFormation development cycle – authoring, validation, deployment, and troubleshooting – without leaving your AI assistant, using the AWS IaC MCP Server.

Introduction Organizations that use feature flags alongside incident response tooling often connect the two manually. When an outage occurs, engineers must identify which flags are relevant, decide whether to disable them, and coordinate the change across teams. This manual process adds latency at the moment it matters most. You can use AWS DevOps Agent and […]

Modern distributed systems route business transactions through dozens of microservices, message queues, and event streams. When a message fails to process or processing exceeds SLA thresholds, troubleshooting requires correlating logs from tools like Elasticsearch, metrics from Datadog, and infrastructure change events in AWS CloudTrail. Correlating these signals manually across heterogeneous backends, each with different query […]

Introduction Modernizing applications by upgrading language runtimes, migrating SDKs, and refactoring frameworks is important for cloud adoption but can be labor-intensive at scale. Each repository requires analysis of dependencies and transformation needs; custom transformation logic must be built and validated, and changes are often executed sequentially across codebases. If you have hundreds of applications, this […]

MCP (Model Context Protocol) is a protocol designed to standardize interactions with Generative AI models, making it easier to build and manage AI applications. It provides a consistent way to communicate context with different types of models, regardless of where they’re hosted or how they’re implemented. The protocol helps bridge the gap between model deployment […]

Introduction This post demonstrates how to leverage AWS CloudFormation Lambda Hooks to enforce compliance rules at provisioning time, enabling you to evaluate and validate Lambda function configurations against custom policies before deployment. Often these policies impact the way a software should be built, restricting language versions and runtimes. A great example is applying those policies […]

Landing Zone Accelerator on AWS (LZA) enables customers to deploy a flexible, configuration-driven solution to establish a landing zone while also leveraging AWS Control Tower. At AWS Professional Services, we’ve helped customers deploy and configure LZA hundreds of times. A common request we encounter is integrating LZA configuration into customers’ existing GitOps workflows. GitOps has […]