[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-228456-en":3,"doc-seo-228456-105":30,"detail-sidebar-cat-0-en-105":92},{"code":4,"msg":5,"data":6},0,"success",{"doc_id":7,"user_id":8,"nickname":9,"user_avatar":10,"doc_module":4,"category_id":11,"category_name":12,"doc_title":13,"doc_description":14,"doc_content":15,"file_id":16,"file_url":17,"file_type":18,"file_size":19,"view_count":20,"is_deleted":4,"is_public":20,"is_downloadable":20,"audit_status":20,"page_count":21,"language":22,"language_code":23,"site_id":24,"html_lang":23,"table_of_contents":25,"faqs":26,"seo_title":27,"seo_description":14,"update_tm":28,"read_time":29},228456,962085662650,"Dozel","https://ap-avatar.wpscdn.com/davatar_29158cc5080c5b710cf443261637dec0",6,"Technology","FLEX series - Brochure","FLEX series brochures explain how deep learning works, separating training and inference, and how learned representations from images, text, and sound translate into model predictions deployed in real systems. The document presents IEI’s FLEX edge inference solution, emphasizing modular design, strong computing capability with Intel chipsets, flexible deployment in compact rack or panel formats, and expansion for AI add-on cards. It also highlights edge-grade reliability, diverse I/O, RAID storage, and bundled software toolkits such as OpenVINO and QuAl for faster deployment and optimization.","iEiR  \nOpenVINOW  \nBMooiEiFLEFLEX SERIESIEI AI Ready Modular Box PC Based on IntelOpenVINO\"Toolkit  \nWWw.ieiworld.com  \n# How Does Deep Learning Work?\n\nDeep learning is a machine learning technique that can learn useful representations of features directly from images,testand sound.There are two phases in,training and inference.The training servers which is designed for Al creates patternsand algorithms from the dataset,and each layer of data is assigned some random weights and your classifier runs a forwardpassthrough the data,predicting the class labels and scores using those weights,after the training model is built,that will beapplied into systems that are able to predict the result,this is what inference systems do.  \n# Achieving Al with IEI Deep Leaming Solution\n\nThe most likely markets to adopt Al technologies,will be medicine,biology,media,security,defense and transportation.Eachmarket faces a variety of challenges for example,in transportation traffic flow prediction,heavily depends on historical andreal-time traffic data collected from various sensor sources,including inductive loops,radars,cameras,etc.It is difficult to finda safe and reliable hardware for the kind of harsh and strict environments.  \nTherefore,IEI introduces the FLEX series which is specifically designed for edge learning inference computation and featuresmodularized,rich interconnectivity,and powerful computing capability.With desktop-class IntelQ370 chipset and cuttingedge technology,the FLEX series supports multiple PCle 3.0 slots and four hot-swappable hard disk drive bays.By applyingmodularized design,the FLEX series will help to accelerate development schedules to reduce total cost of operation.Inaddition,various input/output interfaces are provided for customers to integrate cameras,sensors and motion controlequipment to fast respond to accidental event.  \nFLE  \n# Critical Success Factors for Edge Inference Systems\n\nThe FLEX series offers six features to help Al developers to build diverse Al solutions.  \nInterconnectivity  \nIndustrial grade  \nMeet MIL-810F vibration test and support extendedoperating temperature from-10°℃~50℃ to assureassures system reliability and endurance under thehighest level in volatile,harsh and critical environments.  \nIE's FLEX series offer diverse I/O,including COM,USB,GbELAN,HDMI and audio ports,highly interconnected witharrays of sensors and peripherals  \nHigh volume RAID 0/1/5/10 storage capacity  \n## Flexible deployment\n\nAI systems are highly dependent on enormous volumesof data.IEI's inference computing system,the FLEX series,is equipped with 4 hot-swappable HDDs and dual NVMeSSDs supporting massive storage capacity required for Alworkloads.  \nCompact 2U system for flexible deployment allowsit to be installed everywhere by rack mounting,wallmounting,and even converted to an all-in-one panel PC.  \n## Flexible expansion capability\n\n8th Generation Intel®Core\"DesktopProcessors  \nTwo PCle x8 and two PCle x4 expansion slots allowAI developers to install Al add-on-cards,like VPU,GPU,capture cards and I/O cards,to accelerate Aldevelopment.  \nEquipped with a powerful CPU processor,IE's FLEX systemoffers advanced computing and graphics performance forcomputationally intensive processes.  \n# IEI Al Ready Solution Accelerates Your Al Initiative\n\nThe FLEX-BX200 is an Al hardware ready system ideal for deep learning inference computing to help you get faster,deeperinsights into your customers and your business.IE's FLEX-BX200 supports graphics cards,Intel FPGA acceleration cards,andIntel VPU acceleration cards,and provides additional computational power plus end-to-end solution to run your tasks morefficiently.With the NVIDIA TensorRT,QNAP QuAl,and Intel OpenVINO Al development toolkit,it can help you deploy yoursolutions faster than ever.  \n## ▶OpenVINOTM Toolkit\n\n\"Open Visual Inference &Neural Network Optimization(OpenVINO\"\")toolkit\"allows users to easily deploy open source deeplearning frameworks for Intel architecture t","cbCaijXqnD0lIpwC","https://ap.wps.com/l/cbCaijXqnD0lIpwC","pdf",26404015,1,32,"English","en",105,"# How Does Deep Learning Work?\n# Achieving AI with IEI Deep Learning Solution\n# Critical Success Factors for Edge Inference Systems\n## Flexible deployment\n## Flexible expansion capability\n# IEI AI Ready Solution Accelerates Your AI Initiative\n## OpenVINO Toolkit\n## QTS-Gateway for Cloud-based IPC Solution\n# QuAl Empowers Your AI-related Computing Needs","[{\"question\":\"What are the two main phases in deep learning described in the brochure?\",\"answer\":\"The brochure separates deep learning into training and inference. Training builds patterns and algorithms from the dataset, and inference applies the trained model to predict results in deployed systems.\"},{\"question\":\"What markets does the brochure state are most likely to adopt AI technologies?\",\"answer\":\"It lists medicine, biology, media, security, defense, and transportation. It also uses transportation as an example, highlighting reliance on historical and real-time sensor data.\"},{\"question\":\"How does the FLEX series support edge inference and flexible deployment?\",\"answer\":\"The FLEX series is presented as modular and designed for edge inference computation, with options for rack or wall mounting and conversion into an all-in-one panel PC. It also includes hot-swappable storage to support AI workloads requiring massive data capacity.\"}]","FLEX series - Brochure | PDF",1789025529,81,{"code":4,"msg":31,"data":32},"ok",{"site_id":24,"language":23,"slug":33,"title":13,"keywords":34,"description":14,"schema_data":35,"social_meta":87,"head_meta":89,"extra_data":91,"updated_unix":28},"flex-series-brochure","",{"@graph":36,"@context":86},[37,54,69],{"@type":38,"itemListElement":39},"BreadcrumbList",[40,44,48,51],{"item":41,"name":42,"@type":43,"position":20},"https://docshare.wps.com","Home","ListItem",{"item":45,"name":46,"@type":43,"position":47},"https://docshare.wps.com/document/","Document",2,{"item":49,"name":12,"@type":43,"position":50},"https://docshare.wps.com/document/technology/",3,{"item":52,"name":13,"@type":43,"position":53},"https://docshare.wps.com/document/flex-series-brochure/228456/",4,{"url":52,"name":13,"@type":55,"author":56,"headline":13,"publisher":58,"fileFormat":61,"inLanguage":23,"description":14,"dateModified":62,"datePublished":63,"encodingFormat":61,"isAccessibleForFree":64,"interactionStatistic":65},"DigitalDocument",{"name":9,"@type":57},"Person",{"url":41,"name":59,"@type":60},"DocShare","Organization","application/pdf","2026-09-11","2026-09-10",true,{"@type":66,"interactionType":67,"userInteractionCount":20},"InteractionCounter",{"@type":68},"ViewAction",{"@type":70,"mainEntity":71},"FAQPage",[72,78,82],{"name":73,"@type":74,"acceptedAnswer":75},"What are the two main phases in deep learning described in the brochure?","Question",{"text":76,"@type":77},"The brochure separates deep learning into training and inference. Training builds patterns and algorithms from the dataset, and inference applies the trained model to predict results in deployed systems.","Answer",{"name":79,"@type":74,"acceptedAnswer":80},"What markets does the brochure state are most likely to adopt AI technologies?",{"text":81,"@type":77},"It lists medicine, biology, media, security, defense, and transportation. It also uses transportation as an example, highlighting reliance on historical and real-time sensor data.",{"name":83,"@type":74,"acceptedAnswer":84},"How does the FLEX series support edge inference and flexible deployment?",{"text":85,"@type":77},"The FLEX series is presented as modular and designed for edge inference computation, with options for rack or wall mounting and conversion into an all-in-one panel PC. 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