AWS Step Functions

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

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

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

2026-09-21

COMPANIES · 1 SOURCE · AWS Machine Learning

BMW’s CLEA uses Prophet to detect cost anomalies across 14,000 cloud accounts

BMW Group’s in-house FinOps system, Cloud Efficiency Analytics (CLEA), ingests billing exports (including AWS CUR) and builds per-account-service daily forecasts with Prophet to run daily anomaly detection across roughly 14,000 cloud accounts. The serverless pipeline (Step Functions, Lambda, S3, Distributed Map) processes hundreds of thousands of account-service time series in about 20 minutes and alerts owners when actual spend departs from the forecast; the team reports the compute run costs around $50/month.

4.0

2026-09-21

COMPANIES · 1 SOURCE · AWS Machine Learning

EXL launches AI-powered Medical IDP solution on AWS

EXL has combined its Xtrakto.AI intelligent document processing application with a domain-tuned EXL Insurance LLM and deployed the pipeline on AWS (using Amazon SageMaker AI and Amazon Bedrock among other services) to extract, summarize, and query medical records at enterprise scale, aiming to cut claims and underwriting review time. The end-to-end solution runs within an AWS Region and uses AWS services for ingestion, orchestration, OCR, model training/inference, and secure access control.

5.0

2026-09-17

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

2026-09-16

CODING · 1 SOURCE · AWS Machine Learning

Build a serverless PII redaction pipeline with Amazon Bedrock Data Automation

AWS outlines a recipe for automating end-to-end PII detection and redaction at scale using Amazon Bedrock Data Automation (BDA) custom blueprints combined with a serverless batch pipeline built on AWS Step Functions and Lambda. The post walks through designing blueprint field scopes (example: Attending Physician Statements), extracting field content with bounding boxes and confidence scores, and applying transformations for precise redaction.

5.0