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Blog 2 of N: Structured Output and Your First Agent on SAP AI Core

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By Import Csv, Build Config
| SAP Software Solutions
Blog 2 of N: Structured Output and Your First Agent on SAP AI Core What Are We Building? A sales order analysis agent. You ask it about your sales data - "How were sales in January?" - and it fetches the data, calculates summaries (total orders, revenue, top customer), and responds with a typed, structured answer. It streams the response in real time and remembers your conversation. We'll build this incrementally. Each section teaches a concept (theory), then you apply it to the sales app (lab).
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BeeGuard: Edge-AI Hive Stress Monitor (ESP32 + Audio + MQTT)

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By Import Csv
Things used in this project DFRobot ESP32-S3 AI Camera Module × 1 Key Features Edge AI beehive stress detection using audio signals ESP32-S3 AI Camera with PDM microphone input On-device inference using Edge Impulse (no cloud ML) Signal gating (RMS + ZCR) to ignore silence and irrelevant noise Robust decision logic using M-of-N voting and cooldown Global monitoring via MQTT (HiveMQ public broker) Low latency, low bandwidth, suitable for remote deployments Dataset Preparation The BeeGuard...
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91-基于Spark的空气质量数据分析可视化系统-CSDN博客

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By Import Csv, Struct Field
| CSDN
基于Spark的空气质量数据分析可视化系统设计与实现 项目概述 本项目是一个基于Apache Spark的大数据分析和可视化系统，专门用于空气质量数据的采集、分析、预测和可视化展示。系统采用分布式计算架构，结合机器学习算法，实现了对全国12个主要城市空气质量数据的全面分析和预测功能。 项目背景与意义 随着城市化进程的加快和工业化的快速发展，空气质量问题日益成为公众关注的焦点。传统的空气质量监测方式存在数据分散、分析效率低、可视化效果差等问题。本项目旨在构建一个完整的大数据分析和可视化平台，为空气质量监测提供科学、高效的技术解决方案。 项目目标 数据采集自动化：实现多城市空气质量数据的自动采集和更新 大数据分析：利用Spark分布式计算能力，处理大规模空气质量数据 智能预测：基于机器学习算法，预测空气质量变化趋势 可视化展示：提供直观、交互式的数据可视化界面 系统集成：构建完整的数据科学流程，从采集到展示 项目特色 技术先进性：采用最新的Spark 3.x版本和机器学习技术 架构完整性：涵盖数据采集、存储、分析、预测、可视化的完整流程 扩展性强：支持新城市、新指标的快速接入...
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