Tag: faang

When you build Retrieval Augmented Generation (RAG) applications, answer quality depends on the data that you ingest. Amazon Bedrock Knowledge Bases is the fully managed RAG capability of Amazon Bedrock. With Amazon Bedrock Managed Knowledge Base, you connect your data sources, and Amazon Bedrock manages ingestion, storage, indexing, and retrieval for you. You don’t provision …

Read more “Prepare web content for Amazon Bedrock Knowledge Bases with Crawl4AI”

Today, we’re announcing that spend caps on Google Cloud Budgets are generally available.  With this release, we’re delivering ways to protect your workloads from runaway costs without risking resource deletion. More importantly, we’re introducing three major updates, directly inspired by your feedback since the initial preview, to bring programmability, real-time visibility, and automated guardrails to …

Read more “Innovate on your own business terms: Google Cloud spend caps now generally available”

Cost management for generative AI is a shared responsibility between the teams that build applications and the teams accountable for cloud spend. Many teams start by comparing per-model token rates and asking application teams to shorten prompts. Those steps help, but they don’t answer the questions that finance and engineering leaders ask as usage scales: …

Read more “Managing Amazon Bedrock costs end to end: A playbook for FinOps and platform teams”

The Amazon Quick User Group – United Kingdom is a community of Amazon Quick users in the United Kingdom who meet quarterly to exchange knowledge, share best practices, learn through hands-on sessions, and discuss their experiences using the service. These user groups function as collaborative forums where members learn, connect, and innovate together. Whether you’re …

Read more “How the UK Amazon Quick User Group grew in 2026”

If you’ve tried to get an agent into production, the model and tool use are not the blocker. The main bottleneck is everything around them: how many teams can build within a secure environment, which team owns each model and tool, how AgentOps and error handling stay inside a pre-defined boundary, and how platform, Workstream, …

Read more “Building an Agentic AI Platform on Amazon Bedrock AgentCore”

I’m proud to share that Amazon Web Services (AWS) has been named a Leader in the 2026 Gartner Magic Quadrant for Contact Center as a Service (CCaaS) for Amazon Connect for the fourth consecutive year. We believe this recognition reflects years of work pushing on what’s possible in customer experience. When we launched Amazon Connect …

Read more “AWS named a Leader in the 2026 Gartner Magic Quadrant for Contact Center as a Service”

By Praneeth Yenugutala, Victor Dusa, Anastasia Samoilova, Megan Ren, Justin Wei, Marcelo Mayworm At Netflix, data and machine learning practitioners run millions of workloads each day across orchestration systems and compute engines such as Titus, Spark, Trino, and Druid. On peak days, roughly 100,000 workloads fail. When workloads fail, the failure itself is only the beginning. …

Read more “Making Failure Diagnosis Reliable with Pensive AI”

Fleet-wide computer vision model lifecycle management is a problem that shows up the moment a quality inspection proof of concept succeeds. One camera and one model work fine. Then the plant asks for the same result on 40 lines, each with several cameras, and every camera sees the world differently. Each has a different angle, …

Read more “Fleet-wide computer vision lifecycle at the edge with SageMaker SDK v3”