In today’s day and age, it’s critical to have a modern, scalable and high-performance #objectstorage optimized for #AI infrastructure—which is why organizations are moving to MinIO. Transitioning from the now legacy Hitachi Content Platform (HCP) to MinIO has never been easier, thanks to our HCP-to-MinIO tool. Developed to support our customers' evolving storage needs, this tool is freely available on GitHub and greatly simplifies the migration process. https://hubs.li/Q02FbnB20
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In today’s day and age, it’s critical to have a modern, scalable and high-performance #objectstorage optimized for #AI infrastructure—which is why organizations are moving to MinIO. Transitioning from the now legacy Hitachi Content Platform (HCP) to MinIO has never been easier, thanks to our HCP-to-MinIO tool. Developed to support our customers' evolving storage needs, this tool is freely available on GitHub and greatly simplifies the migration process. https://hubs.li/Q02DQV-V0
Migrate to AI-Ready infrastructure: Hitachi Content Platform to MinIO
blog.min.io
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HCP to MinIO tool is here! Software-defined, S3-compatible object storage offers higher performance, greater flexibility and cloud neutrality compared to dedicated storage appliances.
In today’s day and age, it’s critical to have a modern, scalable and high-performance #objectstorage optimized for #AI infrastructure—which is why organizations are moving to MinIO. Transitioning from the now legacy Hitachi Content Platform (HCP) to MinIO has never been easier, thanks to our HCP-to-MinIO tool. Developed to support our customers' evolving storage needs, this tool is freely available on GitHub and greatly simplifies the migration process. https://hubs.li/Q02DQV-V0
Migrate to AI-Ready infrastructure: Hitachi Content Platform to MinIO
blog.min.io
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In this era where AI is being developed everywhere, how can you overlook MinIO?
In today’s day and age, it’s critical to have a modern, scalable and high-performance #objectstorage optimized for #AI infrastructure—which is why organizations are moving to MinIO. Transitioning from the now legacy Hitachi Content Platform (HCP) to MinIO has never been easier, thanks to our HCP-to-MinIO tool. Developed to support our customers' evolving storage needs, this tool is freely available on GitHub and greatly simplifies the migration process. https://hubs.li/Q02DQV-V0
Migrate to AI-Ready infrastructure: Hitachi Content Platform to MinIO
blog.min.io
-
In today’s day and age, it’s critical to have a modern, scalable and high-performance #objectstorage optimized for #AI infrastructure—which is why organizations are moving to MinIO. Transitioning from the now legacy Hitachi Content Platform (HCP) to MinIO has never been easier, thanks to our HCP-to-MinIO tool. Developed to support our customers' evolving storage needs, this tool is freely available on GitHub and greatly simplifies the migration process. https://hubs.li/Q02Fbnz60
Migrate to AI-Ready infrastructure: Hitachi Content Platform to MinIO
blog.min.io
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We are thrilled to introduce you to our new AI Infrastructure Control Plane. Designed as a universal operating system for #ai infrastructure, this solution provides AI builders, IT teams, and DevOps teams with unmatched control, visibility, and efficiency in managing, orchestrating, and scheduling #gpu compute resources across hybrid environments. Your AI and IT teams now have access to a fully flexible and customizable solution with a future-proof AI Infrastructure Control Plane that is cloud-, silicon-, and vendor-agnostic. Want to learn more? Read the news release here: https://lnkd.in/d3jsSuY7 #aiinfrastructure #gpu #aitechstack #aicompute #machinelearning #mlops #llmops
ClearML Unveils Pioneering AI Infrastructure Control Plane for Ultimate AI Compute Resource Management and Utilization
einpresswire.com
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Revolutionize the development of your edge-native #AI and #ML applications with a secure application delivery platform powered by VMware #Edge Compute Stack and VMware vSAN! Experience the simplicity of building smarter apps today: https://bit.ly/3PWQJI0
Future-Proofing for AI: VMware Edge with vSAN Leads Enterprises to the Next Frontier
blogs.vmware.com
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Technology evangelist -consultant | Industry 4.0/5.0 Beyond Connectivity with AI/ML | ICT Solutions ( 4G/5G/WiFi )/ VNF/CNF/cloud infra.
This is imminent as AI on cloud is worth sustaining and required for success of VRAN and ORAN. The attention is on the impact over cloud build , its computing capacity or worth saying accelerated computing capacity. The development of hw ecosystem for that sake and worth noticing adaptation of AAL by O-ran alliance as standards.
The Role of CNAI in open RAN Evolution Cloud Native Artificial Intelligence (CNAI) framework established by Cloud Native Computing Foundation (CNCF) integrates AI with cloud-native principles to optimize the deployment, operation, scaling, and monitoring of AI workloads on cloud infrastructures. For telecoms, this approach can revolutionize how RANs are managed and scaled, addressing both current AI demands and future growth. ◼ Key Features and Benefits of CNAI in open RAN - Scalability and Performance Dynamic Resource Management: CNAI enables telcos to dynamically scale RAN resources according to the demands of network traffic and user behavior. This adaptability is crucial for handling data traffic spikes without compromising on network performance. - Flexibility and Isolation Containerization: Technologies like Kubernetes allow for the deployment of AI-driven RAN functions within containers. This not only ensures operational isolation but also supports the coexistence of multiple network functions without conflict, irrespective of underlying dependencies. - Efficient Scheduling: Advanced Scheduling: Kubernetes enhances RAN management by optimizing the allocation and utilization of resources such as CPUs and GPUs, which are essential for processing AI workloads. - High-Quality Data Access Data Lakes and Warehouses: Integrating with modern data management systems, CNAI facilitates securely access to vast data resources necessary for training and improving AI models within the RAN. - Cost-Effectiveness Resource Optimization: By leveraging shared cloud resources efficiently, CNAI can cut down on the costs associated with network expansions and upgrades, particularly in underutilized areas. - DevOps Integration: Embedding AI into the cloud-native ecosystem streamlines the development and maintenance of network functions, accelerating deployment cycles and enhancing system reliability. ◼ AI Models as Containerized Artifacts This practice simplifies the management and deployment of AI functionalities, making them more adaptable to different network conditions and user demands. ◼ Evolving Kubernetes Tools With tools designed to support complex AI and ML workloads, such as the Kubernetes Scheduler, telcos can better manage the computational demands of AI-native applications. ◼ Data Management Innovations AI models benefit from scalable cloud storage solutions that manage large datasets efficiently, which is crucial for real-time data processing and analytics in a RAN context. ◼ The Strategic Impact of AI-Native Open Cloud RAN The shift to AI-native Open Cloud RAN signifies a transformative approach to how telcos envision future networks. This architecture not only supports the current 5G expansion but also lays the groundwork for future Telcom networks. #ORAN #OpenRAN #AIRAN #AInativeRAN #CloudRAN #RANEvolution #CloudNativeAI
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Excellent Analysis covering The Role of CNAI in open RAN Evolution 👍 #Cloud #ORAN #5GRAN #5G #6G #AI #CloudComputing #VRAN
The Role of CNAI in open RAN Evolution Cloud Native Artificial Intelligence (CNAI) framework established by Cloud Native Computing Foundation (CNCF) integrates AI with cloud-native principles to optimize the deployment, operation, scaling, and monitoring of AI workloads on cloud infrastructures. For telecoms, this approach can revolutionize how RANs are managed and scaled, addressing both current AI demands and future growth. ◼ Key Features and Benefits of CNAI in open RAN - Scalability and Performance Dynamic Resource Management: CNAI enables telcos to dynamically scale RAN resources according to the demands of network traffic and user behavior. This adaptability is crucial for handling data traffic spikes without compromising on network performance. - Flexibility and Isolation Containerization: Technologies like Kubernetes allow for the deployment of AI-driven RAN functions within containers. This not only ensures operational isolation but also supports the coexistence of multiple network functions without conflict, irrespective of underlying dependencies. - Efficient Scheduling: Advanced Scheduling: Kubernetes enhances RAN management by optimizing the allocation and utilization of resources such as CPUs and GPUs, which are essential for processing AI workloads. - High-Quality Data Access Data Lakes and Warehouses: Integrating with modern data management systems, CNAI facilitates securely access to vast data resources necessary for training and improving AI models within the RAN. - Cost-Effectiveness Resource Optimization: By leveraging shared cloud resources efficiently, CNAI can cut down on the costs associated with network expansions and upgrades, particularly in underutilized areas. - DevOps Integration: Embedding AI into the cloud-native ecosystem streamlines the development and maintenance of network functions, accelerating deployment cycles and enhancing system reliability. ◼ AI Models as Containerized Artifacts This practice simplifies the management and deployment of AI functionalities, making them more adaptable to different network conditions and user demands. ◼ Evolving Kubernetes Tools With tools designed to support complex AI and ML workloads, such as the Kubernetes Scheduler, telcos can better manage the computational demands of AI-native applications. ◼ Data Management Innovations AI models benefit from scalable cloud storage solutions that manage large datasets efficiently, which is crucial for real-time data processing and analytics in a RAN context. ◼ The Strategic Impact of AI-Native Open Cloud RAN The shift to AI-native Open Cloud RAN signifies a transformative approach to how telcos envision future networks. This architecture not only supports the current 5G expansion but also lays the groundwork for future Telcom networks. #ORAN #OpenRAN #AIRAN #AInativeRAN #CloudRAN #RANEvolution #CloudNativeAI
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Edge Computing in the Age of AI: An Overview Data growth driven by data-intensive applications and ubiquitous sensors to enable real-time insight is growing three times faster than access networks. This drives data processing at the edge to keep up with the pace and reduce cloud cost and latency. IDC estimates that by 2027 62% of enterprises' data will be processed at the edge! In this post, I wanted to establish a common taxonomy and architecture building block that takes part in a new generation edge platform. In the follow-up post, I'll cover more specifically the operational and AI inferencing aspects of this architecture. https://lnkd.in/dpraVQcG #iworkfordell #edge #cloudcomputing #ai
Edge Computing in the Age of AI: An Overview | Dell Technologies Info Hub
infohub.delltechnologies.com
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The Role of CNAI in open RAN Evolution Cloud Native Artificial Intelligence (CNAI) framework established by Cloud Native Computing Foundation (CNCF) integrates AI with cloud-native principles to optimize the deployment, operation, scaling, and monitoring of AI workloads on cloud infrastructures. For telecoms, this approach can revolutionize how RANs are managed and scaled, addressing both current AI demands and future growth. ◼ Key Features and Benefits of CNAI in open RAN - Scalability and Performance Dynamic Resource Management: CNAI enables telcos to dynamically scale RAN resources according to the demands of network traffic and user behavior. This adaptability is crucial for handling data traffic spikes without compromising on network performance. - Flexibility and Isolation Containerization: Technologies like Kubernetes allow for the deployment of AI-driven RAN functions within containers. This not only ensures operational isolation but also supports the coexistence of multiple network functions without conflict, irrespective of underlying dependencies. - Efficient Scheduling: Advanced Scheduling: Kubernetes enhances RAN management by optimizing the allocation and utilization of resources such as CPUs and GPUs, which are essential for processing AI workloads. - High-Quality Data Access Data Lakes and Warehouses: Integrating with modern data management systems, CNAI facilitates securely access to vast data resources necessary for training and improving AI models within the RAN. - Cost-Effectiveness Resource Optimization: By leveraging shared cloud resources efficiently, CNAI can cut down on the costs associated with network expansions and upgrades, particularly in underutilized areas. - DevOps Integration: Embedding AI into the cloud-native ecosystem streamlines the development and maintenance of network functions, accelerating deployment cycles and enhancing system reliability. ◼ AI Models as Containerized Artifacts This practice simplifies the management and deployment of AI functionalities, making them more adaptable to different network conditions and user demands. ◼ Evolving Kubernetes Tools With tools designed to support complex AI and ML workloads, such as the Kubernetes Scheduler, telcos can better manage the computational demands of AI-native applications. ◼ Data Management Innovations AI models benefit from scalable cloud storage solutions that manage large datasets efficiently, which is crucial for real-time data processing and analytics in a RAN context. ◼ The Strategic Impact of AI-Native Open Cloud RAN The shift to AI-native Open Cloud RAN signifies a transformative approach to how telcos envision future networks. This architecture not only supports the current 5G expansion but also lays the groundwork for future Telcom networks. #ORAN #OpenRAN #AIRAN #AInativeRAN #CloudRAN #RANEvolution #CloudNativeAI
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