A Technology Broker and Management Company Specializing in KM Systems
Favorite DiDi partnered with AWS to build an intelligent contact center quality assurance (QA) system on Amazon Bedrock for its International Business Group’s Customer Experience (CX) department. The system covers Spanish and Portuguese across three business lines (ride-hailing, food delivery, and financial services) and migrates QA capabilities from an opaque
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Shared by AWS Machine Learning September 9, 2026
Favorite This post was co-written by AWS and the HPE Zerto team. If you manage hybrid and multi-cloud infrastructures, you may already be turning to AI systems to assess health, investigate issues, and act on problems faster. HPE Zerto addressed this challenge by building an agentic troubleshooting system powered by
Read More Shared by AWS Machine Learning September 9, 2026
Favorite Choosing the right GPU instance for large language model (LLM) inference is one of the most impactful decisions you make when deploying generative AI at scale. A single generation jump can slash latency, increase throughput, and reduce cost-per-token. However, the real-world magnitude of those gains depends on model architecture,
Read More Shared by AWS Machine Learning September 9, 2026
Favorite Build a continuous integration and continuous delivery (CI/CD) quality gate that deploys an agent with role-based MCP tools, evaluates it, and blocks PRs when evaluation scores drop. You shipped an AI agent on Amazon Bedrock AgentCore runtime. It calls tools through an MCP server protected by OAuth. Now you
Read More Shared by AWS Machine Learning September 9, 2026
Favorite Automating model registration between MLflow and a model registry solves a gap that opens the moment a candidate model leaves experimentation. Data scientists track dozens of candidate runs in MLflow, while governance officers need one authoritative registry to validate, approve, and audit the models that reach production. Managed MLflow
Read More Shared by AWS Machine Learning September 9, 2026
Favorite Governing models across accounts is the natural next step once automatic model registration is in place. In Part 1 we introduced how managed MLflow on Amazon SageMaker AI synchronizes registered models into the SageMaker AI Model Registry. We walked through a single-account setup where AWS Identity and Access Management
Read More Shared by AWS Machine Learning September 9, 2026
Favorite We are excited to announce feature-level writes for Amazon SageMaker Feature Store. Amazon SageMaker Feature Store is a fully managed, purpose-built repository to store, share, and manage machine learning (ML) features, the processed data used for training models and generating predictions. With the new UpdateRecord API, you can now
Read More Shared by AWS Machine Learning September 9, 2026
Favorite As AI systems take on more complex tasks, much of the industry’s progress has come from increasing model scale, training data, context length, and inference-time computation. Instead of externalizing reasoning work as a chain-of-thought (generating extra tokens sequentially and feeding them back into later steps), Pathway’s brain-inspired BDH (Dragon
Read More Shared by AWS Machine Learning September 9, 2026
Favorite GPT-6 Astra from OpenAI brings greater depth and judgment to your most demanding tasks and runs on the Amazon Bedrock inference engine built for high performance, security, and scale. Organizations are already running AI agents that write code, analyze data, and automate complex workflows at production scale on Amazon
Read More Shared by AWS Machine Learning September 9, 2026
Favorite Disaster recovery (DR) at scale is hard. When thousands of microservices span multiple AWS Regions, coordinating a reliable failover becomes a major operational challenge. At Intuit, we operate at this scale. We support products that millions of people rely on to run their businesses and manage their finances. These
Read More Shared by AWS Machine Learning September 5, 2026