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We’re filling in the gaps to help enterprises and managed service providers power a new GPU-as-a-service experience for internal developers and customers. The New Stack's Darryl Taft covered our extended platform capabilities that better support enterprise AI and ML workloads, with a focus on:  ⚒️ GPU resource management 🔓 Democratizing access to ML pipelines 🔧 Assisting with model testing and selection According to Justin Warren founder and principal analyst at PivotNine said, “GPU-accelerated workloads are a growing part of enterprise portfolios and organizations need scalable tools to manage them…It’s good to see Rafay providing enterprises with options beyond the narrow vision of a few major cloud providers.” More ➡️ https://lnkd.in/gXmwV7iW #GPU #PaaS #cloud #automation #K8s #Kubernetes #GenAI #LLMOps #Developers #AI

The New Stack's Darryl Taft covered our news on extending Rafay's PaaS with support for GPU workloads, with a focus on: ⚒️ GPU resource management 🔓 Democratizing access to ML pipelines 🔧 Assisting with model testing and selection 🙏 🙏 NVIDIA #cloud #cloudcomputing #ai #k8s #kubernetes

The 𝗦𝘁𝗮𝘁𝗲 𝗼𝗳 𝗖𝗹𝗼𝘂𝗱 𝗖𝗼𝘀𝘁𝘀 𝗥𝗲𝗽𝗼𝗿𝘁 by Datadog highlights many critical and unbelievable facts. Have a look! 𝗞𝗲𝘆 𝗙𝗮𝗰𝘁𝘀: Overall Cloud Spending & Optimization: 🔹 Efficiency Plateau: Despite efforts to optimize, cloud spending efficiency has plateaued across organizations of all sizes. 🔹 Wasted Spend: A significant portion of cloud spend (30-45%) is wasted due to overprovisioning and idle resources. Compute Costs: 🔹 GPU Surge: GPU instances now account for 14% of total compute costs, highlighting the growing importance of AI/ML workloads. 🔹 Arm Adoption: Spending on Arm-based instances has doubled in the past year, indicating a shift towards more efficient architectures. 🔹 Instance Size: Organizations are increasingly utilizing larger instance sizes, likely to support demanding workloads. Containerization & Orchestration: 🔹 Kubernetes Dominance: Kubernetes adoption continues to rise, with 70% of organizations using it to manage containerized workloads. 🔹 Managed Kubernetes Growth: More organizations are opting for managed Kubernetes services like Amazon EKS, Google GKE, and Azure AKS. Database Trends: 🔹 Managed Database Preference: The majority of organizations (over 60%) are using managed database services, reducing operational overhead. 🔹 Serverless Database Adoption: Serverless databases are gaining traction, offering scalability and cost-efficiency for variable workloads. Storage & Data Management: 🔹 Object Storage Growth: Object storage usage is on the rise, driven by the need for scalable and cost-effective storage solutions. 🔹 Data Transfer Costs: Data transfer costs remain a significant expense, highlighting the importance of optimizing data movement. Cost Optimization Strategies: 🔹 Rightsizing: Adjusting resource allocation to match actual needs remains a key optimization strategy. 🔹 Spot Instances: Leveraging spot instances for non-critical workloads can significantly reduce costs. 🔹 Reserved Instances/Savings Plans: Committing to long-term usage with reserved instances or savings plans offers substantial discounts. 🔗 https://lnkd.in/djcYFRWW #CloudComputing #CostOptimization

Our latest survey, featuring insights from IT professionals with extensive experience in Kubernetes, reveals key trends shaping the modern application landscape: 🔶 80% will build new applications on cloud-native platforms. 🔶 86% prefer hybrid cloud environments for deployment. 🔶 58% plan to migrate VM workloads to Kubernetes within two years. 🔶 98% run critical data-intensive workloads on Kubernetes. Learn why platform engineering is crucial for success and how organizations are navigating these changes ▶️ https://lnkd.in/g_Y4VU7X #kubernetes #portworx #purestorage #cloud #cloudnative #cncf #hybrid #hybridcloud #vm #workloads #applications #containers #platformengineering

Rafay’s PaaS Now Supports GPU Workloads for AI/ML in the Cloud https://lnkd.in/gfTxqiYr Platform as a Service (PaaS) provider Rafay has extended its Kubernetes management platform to better support enterprise AI and ML workloads, with a focus on GPU resource management, democratizing access to ML pipelines, and assisting with model testing and selection. The new capabilities make compute resources for AI instantly consumable by developers and data scientists with enterprise-grade guardrails, said Haseeb Budhani, co-founder and CEO of Rafay Systems. Rafay is a Kubernetes company that helps customers manage their environments, including Kubernetes, CI/CD pipelines and deployment platforms. Three Gaps The company noticed customers deploying AI workloads on Kubernetes using Rafay’s product, and identified three gaps they could address, Budhani told The New Stack. The first gap is efficiently consuming and sharing expensive GPU resources. Rafay extended its existing PaaS to provide GPU resources to internal customers, with features like time limits and cost management. “What we saw happen was our customers were deploying AI workloads on Kubernetes, and using our product to do it, unbeknownst to us,” Budhani said. The second gap is democratizing access to machine learning (ML) pipelines beyond just data scientists. Rafay introduced an AI/ML workbench on top of their platform to make consuming these pipelines easier for everyone in an enterprise. The third gap is testing and selecting the best ML models. Rafay added an “LLM playground” layer between the PaaS and ML workbench to allow users to quickly test and select the best models for their needs, Budhani said. Filling the Gaps Rafay’s newly added support for GPU workloads helps enterprises and managed service providers power a new GPU-as-a-service experience for internal developers and customers. Rafay’s new AI Suite provides standards-based pipelines for machine learning operations (MLOps) and large language model operations (LLMOps) to quicken the development and deployment of AI applications. Moreover, as the global GPU-as-a-service market is expected to reach $17.2 billion by 2030, organizations are seeking scalable solutions to connect their data scientists and developers to accelerated computing infrastructure. Rafay’s PaaS now addresses issues like environment standardization, self-service consumption of compute, secure use of multitenant environments, cost optimization, and auditability for GPU-based workloads. “GPU-accelerated workloads are a growing part of enterprise portfolios and organizations need scalable tools to manage them,” said Justin Warren, founder and principal analyst at PivotNine. Customers also want to maintain tight control over the sovereignty of sensitive data, a challenge that is only growing in complexity. It’s good to see Rafay providing enterprises with options beyond the narrow vision of a few major cloud providers.” The new features fo...

Continued progress on IBM’s Hybrid Cloud and AI strategy. Miles more to go but each quarter is a marker of the continued progress

Inside Maia 100: Revolutionizing AI Workloads with Microsoft's Custom AI Accelerator #AzureInfra #AzureCentric #mvpbuzz #AzureMVP #AzureNews #AzureCentricPodcast #Azure #AzureCloud #Cloud

Inside Maia 100: Revolutionizing AI Workloads with Microsoft's Custom AI Accelerator #AzureInfra #AzureCentric #mvpbuzz #AzureMVP #AzureNews #AzureCentricPodcast #Azure #AzureCloud #Cloud

Inside Maia 100: Revolutionizing AI Workloads with Microsoft's Custom AI Accelerator #AzureInfra #AzureCentric #mvpbuzz #AzureMVP #AzureNews #AzureCentricPodcast #Azure #AzureCloud #Cloud

Inside Maia 100: Revolutionizing AI Workloads with Microsoft's Custom AI Accelerator #AzureInfra #AzureCentric #mvpbuzz #AzureMVP #AzureNews #AzureCentricPodcast #Azure #AzureCloud #Cloud