AI 每日快讯

AI 每日快讯

AI 产品、模型、开源工具和官方动态的时间流。保留历史记录,按分类、日期和标签继续筛选。

3231历史快讯
177开源工具
13当前结果
08 月 26 日 2026-08-26 快讯

AWS Machine Learning 动态:Evaluate any agent framework with Amazon Bedrock AgentCore 评测

原文摘要:Amazon Bedrock AgentCore 评测 decouples agent 评测 from the framework you build on. As long as your agent emits OpenTelemetry telemetry, the service can score it, whet 来源:AWS Machine Learning 动态。建议继续查看原文,重点核对它影响的工具入口、成本、风险和真实使用场景。

AWS Machine Learning 动态:Natera’s intelligent appointment scheduling with Amazon Bedrock AgentCore

原文摘要:Learn how Natera built an automated voice agent on Amazon Bedrock AgentCore that lets patients book mobile phlebotomy appointments through natural conversation. The post covers the 来源:AWS Machine Learning 动态。建议继续查看原文,重点核对它影响的工具入口、成本、风险和真实使用场景。

AWS Machine Learning 动态:How GoDaddy transformed its analytics with Amazon Quick

原文摘要:In this post, you will learn how GoDaddy migrated from their legacy business intelligence (BI) tool to Amazon Quick. This was a two-year transformation that delivered results acros 来源:AWS Machine Learning 动态。建议继续查看原文,重点核对它影响的工具入口、成本、风险和真实使用场景。

AWS Machine Learning 动态:Preparing data for supervised fine-tuning Part 1: Formatting and quality

原文摘要:Data preparation determines the ceiling of any supervised fine-tuning project. This first post in a two-part series covers the foundations of SFT data prep: quality checks, convers 来源:AWS Machine Learning 动态。建议继续查看原文,重点核对它影响的工具入口、成本、风险和真实使用场景。

AWS Machine Learning 动态:Preparing data for supervised fine-tuning Part 2: Advanced data strategies

原文摘要:The advanced side of supervised fine-tuning data prep. This second post in a two-part series covers evaluating data readiness with learning curves, selecting high-value data subset 来源:AWS Machine Learning 动态。建议继续查看原文,重点核对它影响的工具入口、成本、风险和真实使用场景。

AWS Machine Learning 动态:Bring your own model with Amazon SageMaker AI: Script mode in SDK v3

原文摘要:The SageMaker Python SDK v3 redesigns script mode with unified ModelTrainer and ModelBuilder classes. This post walks through two end-to-end examples, a scikit-learn Random Forest 来源:AWS Machine Learning 动态。建议继续查看原文,重点核对它影响的工具入口、成本、风险和真实使用场景。

AWS Machine Learning 动态:Connect Amazon Bedrock AgentCore to cross-account knowledge bases

原文摘要:Learn how Amazon Bedrock AgentCore agents in one account can generate answers from an Amazon Bedrock knowledge base backed by Amazon Redshift Serverless in another account, without 来源:AWS Machine Learning 动态。建议继续查看原文,重点核对它影响的工具入口、成本、风险和真实使用场景。

InfoQ AI ML Data Engineering:Presentation: Can Claude Fix Itself? Using LLMs for Incident Response

原文摘要:Anthropic reliability engineer Alex Palcuie shares practical lessons on using LLMs for real-world incident response. He explains where AI acts as a superhuman for observing logs an 来源:InfoQ AI ML Data Engineering。建议继续查看原文,重点核对它影响的工具入口、成本、风险和真实使用场景。

InfoQ AI ML Data Engineering:Diagrid Catalyst 2.0 Adds Durable and Verifiable Execution for AI Agents

原文摘要:Diagrid Catalyst 2.0 applies Dapr-based recovery, signed 工作流 history and execution attestation across several agent frameworks. Architects should compare it with framework-nat 来源:InfoQ AI ML Data Engineering。建议继续查看原文,重点核对它影响的工具入口、成本、风险和真实使用场景。

MarkTechPost 官方资讯

MarkTechPost:IBM Releases Granite 4.2: Bringing Native Reasoning and Agentic RL to Open Enterprise Models

原文摘要:IBM has released Granite 4.2, a family of open reasoning language models in 3B, 8B, and 30B sizes, all under Apache 2.0. Every model exposes a thinking / low-effort / non-thinking 来源:MarkTechPost。建议继续查看原文,重点核对它影响的工具入口、成本、风险和真实使用场景。