AWS Machine Learning
·
6 days ago
AWS and Stardog demonstrated building a semantic layer for agentic AI by connecting Amazon Aurora and Amazon Redshift through a federated knowledge graph that enables agents running on Amazon Bedrock AgentCore to answer cross-database questions without ETL pipelines. The solution uses an ontology-driven knowledge graph with virtual graphs mapping to live data sources, allowing the foundation model to compose answers across fragmented enterprise data while maintaining business logic rules and access controls. By separating the model layer, meaning layer, and agent runtime layer, organizations can enable AI agents to reason over enterprise data with the same fluency as senior analysts without duplicating business definitions across multiple systems.
AWS Machine Learning
·
6 days ago
Amazon and Unsloth published guidance on deploying quantized large language models on AWS infrastructure using Unsloth's dynamic quantization technique, which reduces model precision selectively by layer rather than uniformly. A 1.5TB model quantized to 4-bit can be reduced to 217GB with only 14% accuracy degradation instead of 86%, as demonstrated with an 8-billion parameter model shrinking from 16GB to 5GB. The post provides four deployment patterns using EC2, SageMaker inference endpoints, EKS, and ECS, with examples including a Qwen model on ml.g5.xlarge at $1.41/hour versus $7.09/hour for full-precision serving.
AWS Machine Learning
·
6 days ago
KTern.AI built agentic AI agents on Amazon Bedrock AgentCore to automate SAP digital transformation workflows including reverse engineering, process analysis, and exception mining. The platform achieved 45 percent reduction in SAP project timelines, 60–70 percent faster discovery and assessment phases, and new agents deploy to production in 4–6 hours versus the previous 2–3 week development cycle. The shift eliminated custom infrastructure overhead and freed 480 engineering hours per month that the company reinvested into agent capabilities.
AWS Machine Learning
·
6 days ago
Amazon SageMaker HyperPod now supports Disaggregated Prefill and Decode (DPD), which separates LLM inference into compute-bound prefill and memory-bound decode phases running on separate GPU pools connected via Elastic Fabric Adapter. The implementation uses vLLM with LMCache to handle long-context, high-concurrency streaming workloads, with KV cache transfer taking single-digit milliseconds on ml.p5.48xlarge instances. Organizations can now independently tune time to first token and inter-token latency while preventing long prompts from blocking concurrent decode requests.
