How Temporal simplifies AI/ML development with code-first approach

Agentic AI is all the rage as it allows LLMs to retrieve data and perform tasks on your behalf. But how do we create agents that can chat with our enterprise data at-scale? With the new Azure AI Foundry Agent Service, it's easy! In today's post I show how to create a custom agent and connect it to data living in a Microsoft Fabric lakehouse...all without writing a single line of code. Give it a read: https://lnkd.in/eYYfSGmv #ai #azure #azureaifoundry #microsoftfabric #fabric #agenticai #agents #talktoyourdata #cloud #lakehouse

SiliconANGLE & theCUBE covers new Domino Cloud updates: autoscaling Apps for seamless scale, App Discovery to boost adoption, spot instances (up to 60% savings), and managed data planes to bring compute to your data for sovereignty and lower transfer costs—backed by built-in governance. Read the coverage: https://lnkd.in/g4V8Q8hw Learn how Domino helps enterprises control cost and scale AI with confidence: https://lnkd.in/gQwqC74H #EnterpriseAI #AIGovernance #CostOptimization #HybridCloud

Your AI and analytics outcomes depend on the performance of your underlying storage. Choosing the right architecture—from scale-out NAS and NVMe to parallel file systems and cloud tiering—is critical for delivering the massive throughput, low latency, and seamless scalability these workloads demand. The right foundation ensures your models are fed data efficiently, accelerating insights while maintaining governance and control. #AI #DataAnalytics #NetworkStorage #MLOps #DataEngineering #BigData #NVMe #ScaleOutNAS #ParallelFileSystem https://lnkd.in/dPxehTWd

Seekable OCI can significantly reduce pull times, especially for large container images common in AI and machine learning applications. By Tiago Miguel Reichert and Lucas Soriano Alves Duarte, thanks to Cloud Native Computing Foundation (CNCF) and Amazon Web Services (AWS)

Excited to share a new blog I wrote with Lucas on how SOCI (Seekable OCI) can speed up container pull times and help optimize GenAI workloads on Kubernetes. If you’re heading to KubeCon North America, join us to dive deeper into this and other techniques to accelerate AI model deployment. 🎤 From Pull to Predict: Accelerating AI Model Deployment on Kubernetes 📅 Add it to your schedule → https://lnkd.in/dks3UCy5

⚡ Two game-changing approaches to Seekable OCI are revolutionizing how we deploy GenAI workloads: 🔄 Parallel Pull → Faster deployments through concurrent layer downloads ⏱️ Lazy Loading → Reduced startup times with on-demand resource loading Why does this matter? In GenAI applications where every second counts, choosing the right container optimization strategy can mean the difference between seamless user experiences and frustrating delays. Proud to share my team's latest research on optimizing container performance for AI workloads What container optimization challenges are you facing in your GenAI projects? #ArtificialIntelligence #CloudComputing #Performance #AWS #EKS #Kubernetes

Red Hat is a significant commercial contributor to vLLM, a high-performance AI inference server that reduces latency for generative AI workloads. Integrated into the Red Hat AI Inference Server, it supports hybrid cloud environments and multiple hardware accelerators. This makes AI inference faster, #GenAI more accessible, and hybrid deployments seamless for enterprises.

As organizations embrace these powerful technologies, they have opportunities to optimize container image sizes, model sizes and network performance to enhance their development and deployment life cycle. By Tiago Miguel Reichert and Lucas Soriano Alves Duarte, thanks to Cloud Native Computing Foundation (CNCF) and Amazon Web Services (AWS)

This post explores asynchronous processing for time-intensive requests and batch processing for scheduled/event-driven workflows. #aws #awscloud #cloud #advanced300 #generativeai #serverless #technicalhowto

Investing in GPUs and cloud infrastructure won’t guarantee AI success if your data can’t keep up...   AI demands the right volume, variety, and speed of data delivery. Yet too often, data is trapped in silos, moving too slowly to power real-time training, deployment, and insights. 💡   In case you missed our recent blog, get the scoop on what you need to unlock AI success: https://lnkd.in/eDFYcseD