Amazon DynamoDB

2026-10-26

MODELS · 1 SOURCE · AWS Machine Learning

Fleet-wide edge computer-vision pipeline sample using Amazon SageMaker Python SDK v3 and Neo

AWS published a sample (aws-samples GitHub) updating its 2023 visual-inspection pattern to the Amazon SageMaker Python SDK v3, replacing the deprecated SageMaker Edge Manager with SageMaker Neo. The dual-branch SageMaker Pipeline trains a per-camera anomaly autoencoder and a ResNet18 classifier, evaluates models through quality gates, compiles approved artifacts with SageMaker Neo to produce device-optimized builds (ONNX + Neo-compiled library), and uses Step Functions, an SQS FIFO queue, a DynamoDB semaphore, Lambda, SNS, and EventBridge for fleet orchestration and paced retraining; deployment is via AWS CDK and a full synthetic run costs about $0.07 in compute.

6.0

2026-10-23

COMPANIES · 1 SOURCE · AWS Machine Learning

Eon uses Amazon Bedrock AgentCore Gateway to centralize multi-cloud MCP access

Eon implemented a centralized authenticated gateway using Amazon Bedrock AgentCore (Gateway, Runtime, Policy, Identity) to replace per-tool credentials and local MCP processes, giving engineers and autonomous agents unified access to investigation tooling across AWS, GCP, and Azure. The change reduced developer onboarding for investigation tooling from about two days to roughly two minutes and lets Eon's autonomous SRE agent (Claude on Bedrock) access the same tools with separate identities and permissions.

6.0

2026-10-14

CODING · 1 SOURCE · AWS Machine Learning

Build agentic intelligent document processing on Amazon Bedrock AgentCore with Strands Agents

AWS published a how-to for an agentic intelligent document processing (IDP) solution that uses Amazon Bedrock AgentCore and the open-source Strands Agents SDK to classify, extract, validate, and self-learn from business documents. The architecture ties AgentCore runtime and identity capabilities to AWS services (Textract, S3 Vectors, Aurora DSQL, Lambda, EventBridge, DynamoDB, ECS) in multi-agent workflows so new document formats can be processed and reused with minimal manual template configuration.

6.0

2026-10-07

COMPANIES · 1 SOURCE · AWS Machine Learning

Six-week playbook helps non-engineers build multi-agent AI prototypes with Amazon Bedrock and Strands

A six-week structured program paired business professionals with mentors and production-grade tools (Amazon Bedrock AgentCore, Strands Agents SDK, DynamoDB, Node.js/Python Flask) to close the gap between AI knowledge and hands-on capability; participants built WealthWise, a multi-agent financial advisory prototype using Amazon Nova models and won first place. The article lays out the program structure, barriers encountered, and five principles that supported rapid learning and prototype delivery.

4.0

2026-10-06

COMPANIES · 1 SOURCE · AWS Machine Learning

AWS publishes tutorial to build a voice travel concierge with Bedrock AgentCore and Nova 2.5 Sonic

AWS published a how-to demonstrating a voice travel concierge built with Amazon Bedrock AgentCore (runtime and Gateway), Amazon Nova 2.5 Sonic for real‑time speech, and Amazon Bedrock Knowledge Bases, using the AWS CDK and the Model Context Protocol (MCP) to connect the agent to backend services. The post details architecture, components (authentication, data stores, Lambdas, API Gateway), and a sample airline implementation that keeps the agent loosely coupled to existing systems.

5.0

2026-10-01

CODING · 1 SOURCE · AWS Machine Learning

Amazon Bedrock AgentCore guide for ambient agents with event-driven, human-in-the-loop workflows

AWS published a walkthrough and reference implementation showing how to build "ambient" agents on Amazon Bedrock AgentCore that react to event streams (S3, EventBridge, scheduled tasks), pause for human approval via a single ask_human tool, and resume—using AgentCore Runtime plus serverless AWS components (Lambda, DynamoDB, SQS) and Bedrock foundation models. The post includes a sample deployment path requiring AWS CLI, CDK v2, Docker, and an ECR-hosted agent container, and notes session timeouts align with Lambda's 15-minute limit in the reference.

6.0

2026-10-01

COMPANIES · 1 SOURCE · AWS Machine Learning

Uniopen fine-tunes Amazon Nova 2 Lite for retail content moderation

Uniopen (part of Taiwan’s Uni‑President Enterprises Group) adapted Amazon Nova 2 Lite for its retail moderation taxonomy by supervised fine-tuning in Amazon SageMaker AI and additional prompt‑level output optimization, using Nova 2 Pro to generate candidate corrections that humans verify before inclusion in training data. The deployment pipeline uses Amazon S3, DynamoDB, Argo Workflows on EKS, Argo CD, SNS, CloudWatch and Bedrock Guardrails with hard/soft gate checks, fixed test sets, and mandatory human review for ambiguous cases.

6.0

2026-09-25

COMPANIES · 1 SOURCE · InfoQ AI, ML & Data Engineering

Perplexity replaces DynamoDB with in-house CobbleDB to cut batch-read latency 5x

Perplexity moved its core search serving tier from Amazon DynamoDB to CobbleDB, an internally developed Rust-based distributed key-value store, splitting durable storage (Pillar) from hot serving (CobbleDB) and using Lorry to aggregate batch files in S3. The change reportedly reduced median batch-read latency from 31.4 ms to 5.60 ms, cut p99 tail latency from 123 ms to 24.2 ms, sustained synthetic throughput up to 500k requests/sec, and lowered overall storage costs by at least 20%.

7.0

2026-09-22

COMPANIES · 1 SOURCE · AWS Machine Learning

AWS shows how to feed Amazon Bedrock Data Automation outputs into Salesforce Agentforce via MCP

An AWS blog post outlines an architecture that uses Amazon Bedrock Data Automation and the Model Context Protocol (MCP) to process unstructured evidence (video, audio, images, documents) stored in Amazon S3 and make structured insights available to Salesforce Agentforce inside the Salesforce console. The pattern uses S3 events, AWS Lambda, DynamoDB and EventBridge, and routes agent queries through Amazon Bedrock AgentCore Gateway to retrieve processed outputs via MCP.

6.0

2026-09-17

CODING · 1 SOURCE · AWS Machine Learning

Amazon Quick outlines multi-gate authorization for MCP tool invocations

An Amazon blog post provides a walkthrough to implement defense-in-depth authorization for Model Context Protocol (MCP) tool invocations on Amazon Quick. It describes a Lambda ‘interceptor’ behind an Amazon Bedrock AgentCore Gateway that evaluates OIDC JWT claims through four sequential gates (MFA, geographic restriction, group-to-role mapping, and tool-level permission checks) and shows how to configure Microsoft Entra ID, environment variables, and AWS components to enforce role- and attribute-based controls and parameter-level restrictions.

5.0