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AWS CloudFormation

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4

CODING · 1 SOURCE · AWS Machine Learning

CDK construct orchestrates SageMaker endpoint auto scaling during updates

AWS describes a reusable AWS CDK construct that replaces native Application Auto Scaling resources with three CloudFormation custom resources to deregister and then re-register auto scaling around SageMaker real-time endpoint updates. The approach (Deregister, Endpoint update, Register Scalable Target, Attach Scaling Policy) uses asynchronous custom-resource handlers, a ConfigHash to force re-invocation, and provider-generated Lambda and Step Functions helpers so endpoint configuration and auto scaling parameters can be updated in a single deployment without manual steps or broken CloudFormation stacks.

6.0

CODING · 1 SOURCE · AWS Machine Learning

AWS blog: guide to building a context-aware assistant with OpenClaw on AgentCore

A how-to blog post demonstrates building a personal, memory-enabled AI assistant (example: ‘Sprout’ gardener) by combining OpenClaw with Amazon Bedrock AgentCore runtime and AgentCore memory, with deployment packaged as a single CloudFormation template. The guide shows architecture, cost/consumption tradeoffs, required components (including Claude Haiku 4.5 / Claude Sonnet 4.5 access), and a one-command deploy path for personal use.

7.0

MODELS · 1 SOURCE · AWS Machine Learning

Automating Amazon Textract Custom Queries adapter lifecycle across accounts

An AWS post describes architecture, infrastructure templates (CloudFormation and Terraform), and operational patterns to automate promotion, routing, and production hardening of Amazon Textract Custom Queries adapters across accounts. It shows a pre-classification routing step using DetectDocumentText and storing adapter IDs in AWS Systems Manager Parameter Store to enable zero-downtime adapter updates, plus recommended security controls (KMS, PrivateLink, least-privilege IAM, CloudTrail/CloudWatch).

5.0

COMPANIES · 1 SOURCE · AWS Machine Learning

AWS publishes serverless Git-metrics dashboard solution using Amazon QuickSight

AWS released a serverless, event-driven pipeline that automatically collects Git metrics from GitHub and GitLab, stores results in Amazon S3, and visualizes analytics with Amazon QuickSight. The solution uses EventBridge Scheduler, Step Functions, Lambda, and CloudFormation parameters, and includes change detection, adaptive chunking, and full/incremental loads to provide near-real-time engineering analytics and support observability in the AI-Driven Development Lifecycle framework.

3.0