Google Cloud VertexAI Training: New Capabilities for Large-Scale Model Development

We're excited to announce new capabilities in Google Cloud #VertexAI Training designed to simplify and accelerate large-scale model development. Key highlights from our latest update: 🔹 Flexible, Self-Healing Infrastructure: Leverage fully managed, resilient Slurm environments with automated failure detection and performance-optimized checkpointing. 🔹 Cost-Effective Scheduling: Utilize Dynamic Workload Scheduler (DWS) for fixed future reservations or flexible on-demand capacity. 🔹 Integrated Frameworks: Access optimized recipes for the full model lifecycle (including SFT and DPO) and seamless integration with NVIDIA NeMo. Whether you're fine-tuning standard models or training massive custom foundational models from scratch, these new features are built to get you to production faster. Read the full announcement to see how organizations like Salesforce and AI Singapore are already leveraging these tools to elevate their models. 👇 https://lnkd.in/gVVq3WCw

Big day for AI builders! Google Cloud's Vertex AI Training now supercharges large-scale model dev with self-healing infra, optimized recipes for SFT/DPO, NVIDIA NeMo integration, and up to 30% faster throughput. Salesforce & AI Singapore are already crushing it. Dive in: https://lnkd.in/g9UsygQi #VertexAI #AI #MachineLearning e.g. To achieve optimal cost efficiency, you can provision Google Cloud capacity using our Dynamic Workload Scheduler (DWS). Calendar Mode provides fixed, future-dated reservations (up to 90 days), similar to a scheduled booking. Flex-start provides flexible, on-demand capacity requests (up to 7 days) that are fulfilled as soon as all requested resources become simultaneously available. 

🔹 It's great to see Azure making further strides in the “Sovereign Cloud” domain: Microsoft has just launched new digital sovereignty capabilities, enhancing AI, security, and productivity services to help global customers better meet data residency, privacy, and compliance requirements. 🔹 Additionally, Azure AI Foundry now includes image, real-time, audio, and conversational models, while the Microsoft Agent Framework (public preview) simplifies multi-agent AI orchestration. 🔹 For enterprises seeking to lead digital transformation, these updates mean not only access to more powerful AI capabilities but also the confidence to deploy in increasingly regulated environments. #MicrosoftAzure #SovereignCloud #AI #CloudSecurity #DigitalTransformation #TechInnovation https://lnkd.in/e5ggAFQ2

Corvic AI announced the availability of its Intelligence Composition Platform (ICP) on the Microsoft Azure Marketplace, enabling enterprises to deploy high-accuracy, multimodal agentic AI applications at production scale. This launch strengthens Corvic’s cloud ecosystem presence and empowers organizations with faster, more reliable AI orchestration within their existing Azure infrastructure. “Enterprises shouldn’t have to choose between scalability and accuracy,” said Farshid Sabet, CEO of Corvic AI.  Read the full story on our website: https://lnkd.in/dX3zVypC #CorvicAI #AzureMarketplace #AgenticAI #EnterpriseIntelligence #AIInnovation #MultimodalAI #MicrosoftAzure #CloudComputing #AIinBusiness #TechNews #AIOrchestration #martechpulse #bestplacetowork

Centralized, Real-Time AI Service Monitoring: Why Azure AI Foundry’s New Status Dashboard is a Game Changer for Executives For CIOs, CTOs, and CXOs managing enterprise AI workloads, service reliability isn’t just a feature—it’s a mandate. Azure AI Foundry’s newly launched status dashboard delivers exactly that, putting real-time insights into service uptime, incident timelines, and historical performance directly in your hands. #AI #Azure #CloudReliability The dashboard is purpose-built for development and operations teams running mission-critical AI agents and applications. Gone are the days of sifting through the broader

🚀 New Blog Post: Structure AI Workflows on Azure – From Concept to Deployment Building an AI model is easy. Building an AI workflow that scales, adapts, and delivers business value — that’s where the challenge lies. In our latest blog, we walk through how to: Turn AI ideas into production-ready systems Use Azure Machine Learning, MLOps, and DevOps for governance Deploy models for real-time, batch, or edge inference Monitor and retrain continuously for consistent accuracy Learn how to go from proof of concept to production success with Azure. 👉 Read now: https://lnkd.in/gGm73vb4 #Azure #AIWorkflows #CloudStrategy #MachineLearning #SriJayaramInfotech

Exciting advancements in Vertex AI Training have been announced, aimed at simplifying and accelerating the development of large-scale, highly differentiated models. I found it interesting that these new features integrate Google Cloud's AI compute offerings with sophisticated management tools, making the process more efficient. How do you see these enhancements impacting your AI projects?

AI is no longer an add-on to cloud — it’s becoming the primary reason enterprises choose a cloud platform. 🔹 Amazon Web Services (AWS) is shifting from “AI tools” to “AI ecosystems” — Bedrock, Trainium, and the new AI-optimized Graviton chips signal a full-stack play: infra + model + marketplace. 🔹 Microsoft Azure is going all-in on AI everywhere — Copilot across services, deep OpenAI integration, and GPU-dense regions built specifically for enterprise AI workloads. 🔹 Google Cloud is leaning on its research DNA — Gemini, Vertex AI, and its data cloud are giving it an edge in ML-driven apps, analytics, and generative AI at scale. Big picture: AI demand is reshaping cloud architecture. The questions are changing from “Which region should we deploy in?” to “How do we handle GPU clusters, vector databases, model governance, cost control, and multi-tenant AI apps?” For cloud architects & leaders, this era requires: • AI-first architecture → data pipelines, model serving, GPU scaling, edge+cloud design. • Multi-cloud thinking → not for redundancy, but for best-of-AI-services per vendor. • Responsible + cost-aware AI → security, governance, FinOps, compliance all go up a level. • Platform mindset → building reusable AI foundations, not one-off deployments. The future isn’t Cloud with AI on top. It’s AI powered because of cloud — and the provider that abstracts AI complexity best, wins. Curious to know what the community thinks: Which provider is closest to becoming the default “AI Cloud” — AWS, Azure, GCP or any other? And why? #AI #Cloud #Azure #AWS #GoogleCloud #CloudArchitecture #GenAI #ML #TechTrends #FutureOfCloud

Building AI Applications on Microsoft Azure - Free Best Practices Guide >> https://lnkd.in/eUsiMGG8 This guide is designed for developers, data scientists, and IT professionals who want to harness Azure’s capabilities to build, deploy, and manage AI applications efficiently. Ahmed Adel, Robert-Jan Gerrits MSc MBA, Michael Gillett, Sahil Kumar, Alysa Taylor.

💰 AI Is Changing How We Manage Cloud Costs Scaling fast is exciting --- until the cloud bill arrives. In 2025, it’s not just DevOps teams thinking about efficiency; AI is becoming the key to smarter, leaner infrastructure. Here’s what’s emerging right now: 🔹 Predictive scaling --- AI models forecast usage and adjust resources automatically. 🔹 Intelligent workload placement --- workloads shift to cheaper regions or instances in real time. 🔹 Idle resource detection --- AI finds (and even shuts down) unused containers or services. 🔹 Cost-aware CI/CD --- pipelines that balance speed and spend dynamically. Tools like AWS Compute Optimizer and custom ML models are helping teams save 20–40% on infrastructure while keeping performance high. It’s not about cutting corners --- it’s about cutting waste. Smart engineering isn’t just fast --- it’s efficient. ⚙️ 💡 How’s your team using AI to make cloud operations more cost-efficient? #CloudComputing #AI #DevOps #Kubernetes #AWS #Engineering #Optimization