Michael Schüller’s Post

#redhat @KevinTunks Red Hat can help with Edge computing that can assist with Artificial Intelligence and Machine Learning which can benefit Agencies with Data Personalization and a better overall Citizen Experience

Digital Twins and Machine Learning for IT 𝐈𝐧𝐟𝐫𝐚𝐬𝐭𝐫𝐮𝐜𝐭𝐮𝐫𝐞 𝐄𝐧𝐠𝐢𝐧𝐞𝐞𝐫𝐬 𝐄𝐧𝐠𝐢𝐧𝐞𝐞𝐫𝐬 will love this. They always want to enhance the management and performance of IT systems. The Digital Twin & Machine Learning combo offers: 𝐏𝐫𝐨𝐚𝐜𝐭𝐢𝐯𝐞 𝐏𝐞𝐫𝐟𝐨𝐫𝐦𝐚𝐧𝐜𝐞 𝐌𝐨𝐧𝐢𝐭𝐨𝐫𝐢𝐧𝐠 ·     Digital twins create virtual replicas of IT infrastructure components. ·     Real-time data from the physical infrastructure is fed into the digital twin. ·     Machine learning algorithms analyze the data to identify performance patterns and anomalies. ·     Engineers receive proactive alerts for potential performance issues, allowing them to address them before they impact operations. 𝐏𝐫𝐞𝐝𝐢𝐜𝐭𝐢𝐯𝐞 𝐌𝐚𝐢𝐧𝐭𝐞𝐧𝐚𝐧𝐜𝐞 𝐚𝐧𝐝 𝐅𝐚𝐮𝐥𝐭 𝐃𝐞𝐭𝐞𝐜𝐭𝐢𝐨𝐧 ·     Digital twins provide a detailed representation of IT infrastructure and its interconnected components. ·     Machine learning models trained on historical data can predict maintenance needs and potential faults. ·     Engineers can schedule maintenance activities proactively and avoid system downtime. ·     Early fault detection enables timely troubleshooting and prevents major disruptions. 𝐎𝐩𝐭𝐢𝐦𝐢𝐳𝐚𝐭𝐢𝐨𝐧 𝐨𝐟 𝐑𝐞𝐬𝐨𝐮𝐫𝐜𝐞 𝐀𝐥𝐥𝐨𝐜𝐚𝐭𝐢𝐨𝐧 ·     Digital twins offer a holistic view of the IT infrastructure, including hardware, software & network components. ·     Machine learning algorithms analyze resource utilization patterns and identify optimization opportunities. ·     Engineers can optimize resource allocation, ensuring efficient use of computing power, storage & network bandwidth. ·     This leads to improved performance, cost savings & better scalability. 𝐒𝐢𝐦𝐮𝐥𝐚𝐭𝐢𝐨𝐧 𝐚𝐧𝐝 𝐓𝐞𝐬𝐭𝐢𝐧𝐠 𝐄𝐧𝐯𝐢𝐫𝐨𝐧𝐦𝐞𝐧𝐭𝐬 ·     Digital twins provide a virtual environment for simulating and testing changes in the IT infrastructure. ·     Machine learning algorithms can predict the impact of proposed changes based on historical data. ·     Engineers can experiment with different configurations, software updates, or workload changes in a risk-free environment. ·     This enables better decision-making and minimizes the risk of disruptions during actual implementation. 𝐈𝐧𝐜𝐢𝐝𝐞𝐧𝐭 𝐑𝐞𝐬𝐩𝐨𝐧𝐬𝐞 𝐚𝐧𝐝 𝐓𝐫𝐨𝐮𝐛𝐥𝐞𝐬𝐡𝐨𝐨𝐭𝐢𝐧𝐠 ·     Digital twins capture real-time data on IT infrastructure performance and user interactions. ·     Machine learning models analyze the data to identify patterns associated with incidents or anomalies. ·     Engineers can leverage these insights for faster incident response and troubleshooting. ·     Root cause analysis is facilitated by identifying patterns that contribute to system failures or performance degradation. With these both, IT infrastructure engineers can enhance system reliability, optimize resource utilization while delivering efficient and resilient IT services. #digitaltwin #machinelearning #softwareengineering #technology

Bloxi AIsimulated intelligence Survey: Strong computer based intelligence Stage for Content Creation interface 🖇️ https://getbloxiai.com/fe Hey and welcome to my Bloxi computer based intelligence Survey. I'm Darius from Reed Appraisals where I give legitimate computerized item audits alongside wonderful rewards for items I genuinely have confidence in. What's more, today we have Bloxi man-made intelligence. Along these lines, I want to assist you with pursuing an educated choice so you don't wind up squandering your cash. Presentation Bloxi man-made intelligence is an inventive stage that bridles the force of computerized reasoning to reform content creation. Whether you're an entrepreneur or a singular hoping to produce great substance rapidly and easily, Bloxi man-made intelligence has got you covered. This state of the art instrument is intended to save you time and exertion via computerizing the substance creation process, making it the ideal answer for occupied experts who need to consistently produce content. Plan and Quality Bloxi computer based intelligence stands apart with its easy to use and instinctive plan. The dashboard is novice agreeable, guaranteeing that even those with restricted specialized aptitude can explore and use the stage easily. You don't need to stress over downloading or introducing any product as Bloxi simulated intelligence is altogether online, permitting you to get everything rolling in practically no time. The stage's quality radiates through its noteworthy reconciliation of Google, Microsoft, and Amazon computer based intelligence advancements, furnishing clients with unmatched exactness and proficiency. interface 🖇️ https://getbloxiai.com/fe On https://lnkd.in/d673GrVw

SAS delivers leading data management and analytics solutions across the entire analytics life cycle, providing customer-driven solutions you can trust. Some of these to mention are; •Data access, data preparation, data quality and information cataloging •Advanced flow steps and information governance •Visualization and reporting •Model deployment and monitoring •Conversational AI and chatbots •Machine learning and deep learning •Optimization •Statistics •& much more! #sas #analytics #datamanagement #ai #machinelearning #data #deploy https://lnkd.in/gmggW_dt

Edge Video Analytics: A Survey on Applications, Systems and Enabling Techniques Abstract: Video, as a key driver in the global explosion of digital information, can create tremendous benefits for human society. Governments and enterprises are deploying innumerable cameras for a variety of applications, e.g., law enforcement, emergency management, traffic control, and security surveillance, all facilitated by video analytics (VA). This trend is spurred by the rapid advancement of deep learning (DL), which enables more precise models for object classification, detection, and tracking. Meanwhile, with the proliferation of Internet-connected devices, massive amounts of data are generated daily, overwhelming the cloud. Edge computing, an emerging paradigm that moves workloads and services from the network core to the network edge, has been widely recognized as a promising solution. The resulting new intersection, edge video analytics (EVA), begins to attract widespread attention. Nevertheless, only a few loosely-related surveys exist on this topic. The basic concepts of EVA (e.g., definition, architectures) were not fully elucidated due to the rapid development of this domain. To fill these gaps, we provide a comprehensive survey of the recent efforts on EVA. In this paper, we first review the fundamentals of edge computing, followed by an overview of VA. EVA systems and their enabling techniques are discussed next. In addition, we introduce prevalent frameworks and datasets to aid future researchers in the development of EVA systems. Finally, we discuss existing challenges and foresee future research directions. We believe this survey will help readers comprehend the relationship between VA and edge computing, and spark new ideas on EVA. Published in: IEEE Communications Surveys & Tutorials ( Volume: 25, Issue: 4, Fourthquarter 2023)