Amazon CloudWatch

2026-11-02

COMPANIES · 1 SOURCE · AWS Machine Learning

Circle K automates DevOps triage with Amazon Bedrock, cuts mean time to triage to under 15 minutes

Circle K built an automated, agentic triage system on Amazon Bedrock (initially with Bedrock Flows, later migrated to Bedrock AgentCore) that queries logs, services, and internal runbooks and posts structured summaries to Slack; the team reports mean triage time fell to under 15 minutes (a 67–75% reduction by their metrics). The implementation used managed Bedrock features (Agents/AgentCore and Bedrock Knowledge Bases for RAG) to avoid self-hosting models and orchestration code.

6.0

2026-10-28

CODING · 1 SOURCE · AWS Machine Learning

QA Studio (built with Amazon Nova Act) adds application-level organization and team-based access controls

The third post on QA Studio — a reference QA automation solution built with Amazon Nova Act — describes two scaling features: application objects that group use cases and test suites and report health metrics (pass rate, health score, flaky tests, trend charts) via Amazon CloudWatch, and team-based user management that assigns each application to a single team with role-based permissions. The post links failures to Nova Act trajectory logs and session recordings for faster triage and explains environment-aware metrics for CI/CD workflows.

6.0

2026-10-26

CODING · 1 SOURCE · AWS Machine Learning

Build agentic retrieval on Managed Amazon Bedrock Knowledge Base with AWS CDK

An AWS post demonstrates a Python AWS CDK application that deploys an agentic retrieval system using Managed Amazon Bedrock Knowledge Bases and AgentCore. The sample routes queries across two knowledge bases, uses the AgenticRetrieveStream API for multi-turn, cited answers, and provisions CloudWatch dashboards and on-demand/continuous evaluation for observability.

7.0

2026-10-22

CODING · 1 SOURCE · AWS Machine Learning

Measured autoscaling for TTS on Amazon SageMaker using Kokoro-82M

A how-to demonstrates deploying Kokoro-82M (an 82M-parameter non-autoregressive TTS) as a custom container on an Amazon SageMaker AI real-time endpoint via AWS CDK and replacing a guessed autoscaling target with one derived from two live measurements: Q_max (max per-instance concurrency inside the SLO) and T_total (end-to-end scale-out time). The deployment uses SageMaker's native ConcurrentRequestsPerModel metric (reported to CloudWatch) and supports both InvokeEndpointWithResponseStream and InvokeEndpointWithBidirectionalStream transports; tooling converts client-measured concurrency into CloudWatch units and the example run reported a 1.0 ratio between measured and published values.

6.0

2026-10-13

COMPANIES · 1 SOURCE · AWS Machine Learning

Anchor Browser integrates with Amazon Bedrock AgentCore payments to handle x402 machine-to-machine payments

Anchor Browser now integrates with Amazon Bedrock AgentCore payments so AI agents can autonomously respond to HTTP 402 Payment Required challenges using the x402 protocol. The integration, developed with the AWS Generative AI Innovation Center Partner Agent Factory and available on AWS Marketplace, manages wallets, spending limits, and CloudWatch observability so agent workflows continue without manual intervention.

6.0

2026-10-07

MODELS · 1 SOURCE · AWS Machine Learning

Claude Opus 5.5 and Sonnet 5.5 available on Amazon Bedrock in AWS GovCloud (US) with Claude Code

Anthropic’s Claude Opus 5.5 and Claude Sonnet 5.5 (alongside Claude Sonnet 5) are available via Amazon Bedrock in AWS GovCloud (US); Sonnet 5 holds FedRAMP Class D and DoD IL4/IL5 authorizations and Opus/Sonnet 5.5 hold FedRAMP Class D certification on Bedrock (users should verify current model certification status). The post outlines using Anthropic’s agentic coding tool Claude Code with Bedrock’s bedrock-runtime and bedrock-mantle endpoints, describing security features (Zero Operator Access), compliance pathways, and integrations for CI/CD and developer tooling.

8.0

2026-09-28

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

2026-09-25

MODELS · 1 SOURCE · AWS Machine Learning

Amazon SageMaker JumpStart enables real-time deployment of Qwen3-TTS-12Hz-1.7B-Base for voice cloning

Amazon documents how to deploy the publicly available Qwen3-TTS-12Hz-1.7B-Base text-to-speech model from SageMaker JumpStart to a fully managed, real-time inference endpoint using the SageMaker Python SDK; the model supports few-second reference-based voice cloning, cross-lingual synthesis across ten languages, streaming generation, and monitoring via CloudWatch. The post highlights benefits such as data control within an AWS account, cost alignment with compute usage, and use cases including localization, personalized assistants, and e-learning.

6.0

2026-09-18

CODING · 1 SOURCE · AWS Machine Learning

Hugging Face publishes six open-source Skills to deploy models on Amazon SageMaker AI via coding agents

Hugging Face published six open-source 'Skills' (GitHub) that let coding agents orchestrate end-to-end deployment of Hugging Face models to Amazon SageMaker AI. The skills automate selecting the correct serving container from AWS Deep Learning Containers, creating real-time or serverless endpoints with autoscaling and CloudWatch alarms, and provide verified teardown paths; they run using Python and the AWS CLI and are designed to prevent fragile or costly agent-made deployment mistakes.

6.0