标签

 cloud 

相关的文章:

MongoDB -

How Atlas Edge Server Bridges the Gap Between Connected Retail Stores and the Cloud

Efficient operations and personalized customer experiences are essential for the success of retail businesses. In today's competitive retail industry, retailers need to streamline their operations, optimize inventory management, and personalize the customer experience to stay ahead. In a recent announcement at MongoDB .local London, we unveiled the private preview of MongoDB Atlas Edge Server, offering a powerful platform that empowers retailers to achieve their goals, even when low or intermittent connectivity issues may arise. What is edge computing, and why is it so relevant for retail? The retail industry's growing investment in edge computing, projected to reach $208 billion by 2023, confirms the strategic shift retailers are willing to take to reach new markets and enhance their offers. And for good reason — in scenarios where connectivity is unreliable, edge computing allows operations to continue uninterrupted. Edge computing is a strategic technology approach that brings computational power closer to where data is generated and processed, such as in physical retail stores or warehouses. Instead of relying solely on centralized data centers, edge computing deploys distributed computing resources at the edge of the network. The evolution of investments in edge computing reflects a journey from initial hesitation to accelerated growth. As edge computing continues to mature and demonstrate its value, retailers are likely to further embrace and expand their focus in bringing applications where the computing and data is as close as possible to the location where it's being used. Let’s dig into how MongoDB addresses the current challenges any retailer would experience when deploying or enhancing in-store servers using edge computing. Connected store: How MongoDB's versatile deployment from edge to cloud powers critical retail applications. Currently, many retail stores operate with an on-site server in place acting as the backbone for several critical applications within the store ecosystem. Having an on-site server means that the data doesn't have to travel over long distances to be processed, which can significantly reduce latency. This setup can often also be more reliable, as it doesn't depend on internet connectivity. If the internet goes down, the store can continue to operate since the essential services are running on the local network. This is crucial for applications that require real-time access to data, such as point-of-sale (POS) systems, inventory management, and workforce-enablement apps for customer service. The need for sync: Seamless edge-to-cloud integration The main driver for retailers taking a hybrid approach is that they want to experience the low latency and reliability of an on-site server coupled with the scalability and power of cloud computing for their overall IT stack. The on-site server ensures that the devices and systems that are critical to sales floor operations — RFID tags and readers for stock management, mobile scanners for associates, and POS systems for efficient checkout — remain functional even with intermittent network connectivity. This data must be synced to the retailer’s cloud-based application stack so that they have a view of what’s happening across the stores. Traditionally this was done with an end-of-day batch job or nightly upload. The aim for the next generation of these architectures is to give real-time access to the same data set, seamlessly reflecting changes made server-side or in the cloud. This needs to be achieved without a lag from the store being pushed to the cloud and without creating complex data sync or conflict resolution that needs to be built and maintained. These complexities may cause discrepancies between the online and offline capabilities of the store's operations. It makes sense that for any retailer wanting to benefit from both edge and cloud computing, it must simplify its architecture and focus on delivering value-added features to delight the customer and differentiate from their competitors. Low-latency edge computing with Atlas Edge Server and its different components to achieve data consistency and accuracy across layers This is when Atlas Edge Server steps in to bridge the gap. Edge Server runs on-premises and handles sync between local devices and bi-directional sync between the edge server and Atlas. It not only provides a rapid and reliable in-store connection but also introduces a tiered synchronization mechanism, ensuring that data is efficiently synced with the cloud. These devices are interconnected through synchronized data layers from on-premises systems to the cloud, simplifying the creation of mobile apps thanks to Atlas Device SDK, which supports multiple programming languages, development frameworks, and cloud providers. Additionally, Atlas Device Sync automatically handles conflicts, eliminating the need to write complex conflict-resolution code. In the below diagram, you can see how the current architecture for a connected store with devices using Atlas Device SDK and Atlas Device Sync would work. This is an ideal solution for devices to sync to the Atlas backend. A high-level overview of the Architecture for connected devices in a retail space with MongoDB Device Sync and MongoDB Atlas when connectivity is unreliable. In a store with Atlas Edge Server, the devices sync to Atlas on-premises. All changes made on the edge or on the main application database are synced bidirectionally. If the store server goes offline or loses connectivity, the devices can still access the database and update it locally. The store can still run its operations normally. Then, when it comes back online, the changes on both sides (edge and cloud) are resolved, with conflict resolution built into the sync server. A high-level overview of the architecture for connected devices in a retail space with MongoDB Device Sync and MongoDB Atlas solving connectivity issues by implementing an on-premises Atlas Edge Server. Deploying Atlas Edge Server in-store turns connected stores into dynamic, customer-centric hubs of innovation. This transformation produces advantageous business outcomes including: Enhanced inventory management — The hybrid model facilitates real-time monitoring of logistics, enabling retailers to meticulously track stock in store as shipments come in and sales or orders are processed. By processing data locally and syncing with the cloud, retailers gain immediate insights, allowing for more precise inventory control and timely restocking. Seamless operational workflows — The reliability of edge computing ensures essential sales tools — like RFID systems, handheld scanners, workforce apps, and POS terminals — remain operational even during connectivity hiccups. Meanwhile, the cloud component helps ensure that all data is backed up and accessible for analysis, leading to more streamlined store operations. Customized shopping experiences — With the ability to analyze data on-the-spot (at the edge) and harness historical data from the cloud, retailers can create highly personalized shopping experiences. This approach enables real-time, tailored product recommendations and promotions, enhancing customer engagement and satisfaction. Conclusion With Atlas Edge Server, MongoDB is committed to meeting the precise needs of modern retail stores and their diverse use cases. Lacking the seamless synchronization of data between edge devices and the cloud, delivering offline functionality that enables modern, next-generation workforce applications, as well as in-store technologies like POS systems, is daunting. Retailers need ready-made solutions so they don't have to deal with the complexities of in-house, custom development. This approach allows them to channel their development efforts towards value-added, differentiating features that directly benefit their customers by improving their in-store operations. With this approach, we aim to empower retailers to deliver exceptional customer experiences and thrive in the ever-evolving retail landscape. Ready to revolutionize your retail operations with cutting-edge technology? Discover how MongoDB's Atlas Edge Server can transform your store into a dynamic, customer-centric hub. Don't let connectivity issues hold you back. Embrace the future of retail with Atlas Edge Server!

AI生成摘要 腾讯游戏推出Level Infinite PGOS平台,采用MongoDB作为核心存储组件,优化游戏开发体验。Level Infinite PGOS是一套多租户SaaS游戏后台解决方案,支持玩家数据存储、智能对局匹配、游戏内经济系统闭环和数据流能力。腾讯游戏通过MongoDB实现简单易用的控制台、丰富的可视化监控、一键升配降配能力和多维告警能力,提升运维能力。腾讯游戏Level Infinite PGOS平台负责人表示,MongoDB让平台如虎添翼。

相关推荐 去reddit讨论

McKinsey Insights & Publications -

Getting ahead in the cloud

There is $3 trillion worth of business value at stake for companies that successfully use cloud technology—yet many are still in a fog. Here’s a clear path toward cloud adoption. Â

AI生成摘要 根据麦肯锡的研究,只有20%到30%的行业定期并大规模使用云技术。云技术对企业至关重要,但许多公司在从云计划中获取最大价值方面面临困难。麦肯锡的高级合伙人Mark Gu和James Kaplan分享了关于云价值的研究结果,并讨论了企业面临的挑战以及改善云能力的步骤。同时,麦肯锡的高级合伙人Scott Keller强调了六个关键词对公司成功的重要性。

相关推荐 去reddit讨论

小众软件 -

如何保存 Google Cloud Text-to-Speech AI 文字转语音服务为音频文件

今天尝试了一下给视频配音,临时起意想找一款文字转语音服务,就找到了这个 Text-to-Speech AI,毕竟提供在线试用服务。发现可以很简单的将试用语音保存下来。@Appinn 最近有不少同学在

AI生成摘要 作者尝试了Google Cloud的Text-to-Speech AI服务,发现可以将试用语音保存下来。然而,由于Azure的语音服务更丰富,作者可能会选择Azure来解决配音问题。

相关推荐 去reddit讨论

NVIDIA Blog -

Embracing Transformation: AWS and NVIDIA Forge Ahead in Generative AI and Cloud Innovation

Amazon Web Services and NVIDIA will bring the latest generative AI technologies to enterprises worldwide. Combining AI and cloud computing, NVIDIA founder and CEO Jensen Huang joined AWS CEO Adam Selipsky Tuesday on stage at AWS re:Invent 2023 at the Venetian Expo Center in Las Vegas. Selipsky said he was “thrilled” to announce the expansion Read article >

AI生成摘要 亚马逊网络服务(AWS)和NVIDIA将把最新的生成式人工智能技术带给全球企业。AWS将成为首个采用NVIDIA GH200 NVL32 Grace Hopper Superchip的云服务提供商,AWS还将引入NVIDIA DGX Cloud,并整合NVIDIA的软件库。NVIDIA的创始人兼首席执行官黄仁勋强调了NVIDIA库与AWS的集成,合作将使AWS开放给数百万开发者和近4万家使用这些库的公司。AWS CEO Selipsky介绍了NVIDIA GH200 Grace Hopper Superchip在云计算方面的重大进展。合作将推出首个由GH200 Superchips驱动的DGX Cloud AI超级计算机,展示了AWS云基础设施和NVIDIA人工智能专业知识的强大能力。

相关推荐 去reddit讨论

Redis Blog -

Redis Cloud Leverages Amazon Bedrock to Deliver Speed and Reliability to Retrieval Augmented Generation Architectures

Redis is working with AWS to accelerate generative AI application development The post Redis Cloud Leverages Amazon Bedrock to Deliver Speed and Reliability to Retrieval Augmented Generation Architectures appeared first on Redis.

AI生成摘要 Redis与AWS合作,通过Redis Cloud加速生成式AI应用程序的开发,为AWS客户提供价值。他们整合了Redis Cloud和Amazon Bedrock,以解决开发人员在构建生成式AI应用程序时面临的挑战,包括成本、质量、性能和安全性等方面。他们还为开发人员提供了一系列资源和指南,以帮助他们在云端构建或增强生成式AI应用程序。Redis还宣布获得了AWS数据和分析能力的认证。

相关推荐 去reddit讨论

Redis Blog -

Powering LangChain OpenGPTs With Redis Cloud

Redis Cloud provides the essential persistent datastore for Langchain OpenGPTs The post Powering LangChain OpenGPTs With Redis Cloud appeared first on Redis.

AI生成摘要 OpenGPTs是一个低代码、开源的框架,用于构建定制的AI代理。LangChain选择Redis Cloud作为默认的向量数据库。OpenGPTs允许选择理想的LLM提供商、系统提示和启用的工具。Redis Cloud在OpenGPTs中提供持久性聊天会话、代理配置和向量数据库检索的功能。Redis Cloud与LangChain的集成为OpenGPTs带来了适应性、可扩展性和实时处理和搜索的重要功能。

相关推荐 去reddit讨论

NVIDIA Blog -

Medical Imaging AI Made Easier: NVIDIA Offers MONAI as Hosted Cloud Service

NVIDIA today launched a cloud service for medical imaging AI to further streamline and accelerate the creation of ground-truth data and training of specialized AI models through fully managed, cloud-based application programming interfaces. NVIDIA MONAI cloud APIs — announced at the annual meeting of RSNA, the Radiological Society of North America, taking place this week Read article >

AI生成摘要 NVIDIA推出了医学影像AI云服务,通过完全托管的云应用程序编程接口,进一步简化和加速地创建基准数据和训练专业AI模型。NVIDIA MONAI云API是建立在NVIDIA和伦敦国王学院共同创建的开源MONAI项目之上的。该服务可帮助医学影像解决方案提供商更轻松地将AI集成到其平台中,为放射科医生、研究人员和临床试验团队提供强大的工具。此外,该服务还提供了交互式标注和自动分割等功能,以加速AI模型的开发。

相关推荐 去reddit讨论

jdhao's blog -

How to Download Files from Google Cloud Storage in the Databricks Workspace Notebook

In this post, I want to share the complete process and setup to download files from GCP in a Databricks workspace notebook. Since the notebook itself is non-interactive when you run the shell command, the setup process is a bit different from the normal GCP authentication.

AI生成摘要 本文介绍了在Databricks工作空间笔记本中从GCP下载文件的完整过程和设置。安装gcloud cli,更新依赖库,设置公共GPG密钥,安装gcloud cli,进行身份验证,创建服务帐号密钥,安装gcloud存储包,从云存储下载文件。

相关推荐 去reddit讨论

阿里云云栖号 -

通过 Higress Wasm 插件 3 倍性能实现 Spring-cloud-gateway 功能

本文将和大家一同回顾 Spring Cloud Gateway 是如何满足 HTTP 请求/响应转换需求场景的,并为大家介绍在这种场景下使用 Higress 云原生网关的解决方案,同时还对比了两者的性能差异。

AI生成摘要 本文介绍了Spring Cloud Gateway和Higress云原生网关在满足HTTP请求/响应转换需求场景方面的解决方案,并对比了两者的性能差异。文章详细介绍了Higress云原生网关的Transformer插件的功能和使用方法,并提供了核心代码逻辑。希望能为从Spring Cloud Gateway迁移到Higress提供帮助。

相关推荐 去reddit讨论

Redis Blog -

Redis Cloud Propels LangChain OpenGPTs Project

The post Redis Cloud Propels LangChain OpenGPTs Project appeared first on Redis.

AI生成摘要

相关推荐 去reddit讨论

热榜 Top10
...
ShowMeBug
...
观测云
...
eolink
...
Dify.AI
...
白鲸技术栈
...
天勤数据
...
LigaAI
推荐或自荐