Artificial Intelligence

Category: Artificial Intelligence

How Heurist Finance built an AI-native investment workbench on Amazon Bedrock AgentCore

How Heurist Finance built an AI-native investment workbench on Amazon Bedrock AgentCore

Learn how Heurist built Heurist Finance, a conversational AI investment workbench, on Amazon Bedrock AgentCore. This customer story shows how AgentCore payments, Identity, Memory, Code Interpreter, and Observability let a small team buy premium market data per query, isolate analysis in a sandbox, and keep every action auditable.

Automate user-level custom permissions for Amazon Quick

Automate user-level custom permissions for Amazon Quick

Amazon Quick custom permissions let you enforce least-privilege access by toggling features per user. This post walks through four patterns to automate custom permissions across the user lifecycle: a RegisterUser API parameter, account and role defaults, event-driven Amazon EventBridge and AWS Lambda automation, and a retroactive batch update script.

Pathway’s brain-inspired architecture development on Amazon SageMaker HyperPod

Pathway’s brain-inspired architecture development on Amazon SageMaker HyperPod

Pathway’s Baby Dragon Hatchling (BDH) is a brain-inspired, post-transformer architecture that reasons in latent space instead of emitting chain-of-thought tokens. See how Pathway develops and scales BDH on Amazon SageMaker HyperPod, and how BDH-CQ set a new cost-efficiency mark on the ARC-AGI-1 benchmark.

Amazon SageMaker Feature Store introduces UpdateRecord for feature-level writes

Amazon SageMaker Feature Store introduces UpdateRecord for feature-level writes

Amazon SageMaker Feature Store now supports feature-level writes. With the new UpdateRecord API, you can update one or more feature values in a single call without reading or rewriting the entire record. It is available for both the Standard (Amazon DynamoDB) and In-Memory (Amazon ElastiCache) online store tiers.

Govern models with MLflow and Amazon SageMaker AI Model Registry sync part 2

Govern models with MLflow and Amazon SageMaker AI Model Registry sync: Part 2

Governing models across accounts is the next step after automatic model registration. This post extends managed MLflow and Amazon SageMaker AI Model Registry sync to two cross-account governance topologies: a hub-and-spoke pattern that centralizes governance with AWS RAM, and a hybrid pattern that keeps development accounts isolated.

Govern models with MLflow and Amazon SageMaker AI Model Registry sync part 1

Govern models with MLflow and Amazon SageMaker AI Model Registry sync: Part 1

Managed MLflow on Amazon SageMaker AI now syncs richer model metadata (training metrics, evaluation results, inference specs, and lineage) into the SageMaker AI Model Registry, with lifecycle stage promotion. Part 1 shows how to govern candidate models in a single account using IAM guardrails.

Benchmarking small LLM inference on SageMaker AI: G7 vs G5 and G6

Benchmarking small LLM inference on SageMaker AI: G7 vs G5 and G6

Benchmark two 30B Mixture-of-Experts models, Qwen3-Coder-30B and NVIDIA Nemotron-3-Nano-30B, across G5, G6, G6e, and G7 GPU instances on Amazon SageMaker AI. Compare throughput, latency, and cost-per-token, and see how G7’s NVIDIA Blackwell GPUs deliver measurable price-performance gains for real-time LLM inference.